Carathéodory s extension of a measure on a semi-ring

Size: px
Start display at page:

Download "Carathéodory s extension of a measure on a semi-ring"

Transcription

1 Carathéodory s extension of a measure on a semi-ring Reinhardt Messerschmidt 7 October Introduction This article presents Carathéodory s extension of a measure on a semi-ring, and the construction of the Lebesgue-Stieltjes σ-algebra and measure with respect to a real-valued function on R d satisfying certain nonnegativity and continuity conditions. It follows a combination of the approaches in [B86], [C80], [F99] and [S09]. 2 Some properties of sets of subsets Suppose X is an arbitrary set. Let PX be the power set of X, i.e. the set of all subsets of X. If A is a subset of X, let A c be the complement of A with respect to X, i.e. A c = X A. A subset S of PX is i closed under finite unions if A 1 A 2 S whenever A 1, A 2 S, ii closed under finite intersections if A 1 A 2 S whenever A 1, A 2 S, iii closed under differences if A 1 A 2 S whenever A 1, A 2 S, iv partially closed under differences if for every A 1, A 2 S there exist disjoint sets B 1, B 2,..., B n in S such that A 1 A 2 = n B j, v closed under complements if A c S whenever A S, vi closed under countable unions if S whenever is a sequence in S, vii closed under disjoint countable unions if S whenever is a sequence of disjoint sets in S. Proposition 2.1. If S is a subset of PX that is closed under complements, closed under finite unions, and closed under disjoint countable unions, then it is closed under countable unions. 1

2 Proof. Suppose is a sequence in S. Let B 1 = A 1, and for every j 2 let B j = j 1 A k, then B j = j 1 j 1 A k = A k c = j 1 c A c j A k, B j is a sequence in S. The sets B 1, B 2,... are also disjoint, B j S, but = B j, S. 3 Proto-rings, semi-rings and σ-algebras A subset S of PX is i a proto-ring if S, ii a semi-ring if it is a proto-ring, closed under finite intersections, and partially closed under differences, iii a σ-algebra if it is a proto-ring, closed under complements, and closed under countable unions. Proposition 3.1. If S is a semi-ring and A, B 1, B 2,..., B n S, then there exist disjoint sets C 1, C 2,..., C m S such that A n B j = m C j. Proof. We will use induction on n. The base case follows from the partial closure of S under differences. For the inductive case, note that n+1 A B j = A n B j B n+1. By the inductive hypothesis, there exist disjoint sets C 1, C 2,..., C m S such that A n B j = m C j, n+1 m m A B i = C j B n+1 = C j B n+1. For every j, there exist disjoint sets D j1, D j2,..., D jmj S such that It follows that C j B n+1 = m j D jk j =1,2,...,m,2,...,m j D jk. is a finite sequence of disjoint sets in S whose union is A n+1 B j. Proposition 3.2. If S is a σ-algebra then it is closed under finite unions, closed under finite intersections, closed under differences, and a semi-ring. 2

3 Proof. Closed under finite unions. Suppose A 1, A 2 S. Let B 1 = A 1, and for every j 2 let B j = A 2, then A 1 A 2 = B j and B j S, A 1 A 2 S. Closed under finite intersections. This follows from closure under finite unions and the identity A 1 A 2 = A c 1 A c 2 c. Closed under differences. This follows from closure under finite intersections and the identity A 1 A 2 = A 1 A c 2. Semi-ring. Closure under differences implies partial closure under differences. 4 Some properties of functions on sets of subsets If S is a subset of PX, then a function µ from S into [0, ] is i monotone if µa 1 µa 2 whenever A 1, A 2 S and A 1 A 2, ii finitely additive if n µ = µ whenever A 1, A 2,..., A n are disjoint sets in S such that n S, iii subtractive if µa 1 A 2 = µa 1 µa 2 whenever A 1, A 2 S are such that A 1 A 2 S and µa 2 <, iv countably additive if µ = µ whenever is a sequence of disjoint sets in S such that S, v countably subadditive if µ A i µ whenever is a sequence in S such that S, vi σ-finite if there exists a sequence in S such that µ < for every j and = X. Proposition 4.1. If S is a subset of PX and µ is a finitely additive function on S, then µ is subtractive. Proof. Suppose A 1, A 2 S are such that A 1 A 2 S and µa 2 <. We have µa 1 = µa 2 + µa 1 A 2, and since µa 2 < it can be subtracted from both sides. Proposition 4.2. Suppose S is a semi-ring and µ is a finitely additive function on S. 3

4 a If A S and B 1, B 2,..., B n are disjoint sets in S such that n B j A, then µb j µa. b If A S and B 1, B 2,..., B n are sets in S such that A n B j, then c µ is monotone. µa µb j. Proof. a There exist disjoint sets C 1, C 2,..., C m S such that A n B j = m C j. It follows that B 1, B 2..., B n, C 1, C 2..., C m are disjoint sets in S whose union is A, b Note that µa = m µb j + µc j A = A n B j = µb j. n A B j and A B j S for every j. Let C 1 = A B 1, and for every j 2 let j 1 C j = A B j A B k. For every j, there exist disjoint sets D j1, D j2,..., D jmj in S such that C j = mj D jk. It follows that D jk j =1,2,...,m,2,...,m j is a finite sequence of disjoint sets in S whose union is A, By part a, c Let n = 1 in part a. µa = m j m j µd jk. µd jk µb j, µa m µb j. Proposition 4.3. If S is a semi-ring and µ is a finitely additive and countably subadditive function on S, then µ is countably additive. 4

5 Proof. Suppose is a sequence of disjoint sets in S such that S. By proposition 4.2a, µ µ for every n, µ µ. The opposite inequality holds by countable subadditivity. Proposition 4.4. If S is a semi-ring and µ is a σ-finite and monotone function on S, then there exists a sequence C j of disjoint sets in S such that µc j < for every j and C j = X. Proof. There exists a sequence of sets in S, not necessarily disjoint, such that µ < for every j and = X. Let B 1 = A 1, and for every j 2 let B j = For every j, there exist disjoint sets C j1, C j2,..., C jmj S such that B j = mj C jk. It follows that C jk j =1,2,...,2,...,m j is a sequence of disjoint sets in S whose union is X, and µc jk µ < for every j, k. j 1 A k. 5 Proto-measures and measures If S is a proto-ring, then a function µ from S into [0, ] is i a proto-measure if µ = 0, ii a measure if it is a proto-measure and countably additive. Proposition 5.1. If S is a proto-ring and µ is a measure on S, then µ is finitely additive. Proof. Suppose A 1, A 2..., A n are disjoint sets in S such that n S. For every j = 1, 2,..., n let B j =, and for every j > n let B j =. It follows that B j is a sequence of disjoint sets in S and n µ B j = = µ n S, B j = µb j = µ. Proposition 5.2. If S is a semi-ring and µ is a measure on S, then µ is countably subadditive. 5

6 Proof. Suppose is a sequence in S such that S. Let B 1 = A 1, and for every j 2 let B j = For every j, there exist disjoint sets C j1, C j2,..., C jmj S such that B j = mj C jk. It follows that C jk j =1,2,...,2,...,m j j 1 A k. is a sequence of disjoint sets in S whose union is, µ = m j µc jk. By proposition 4.2a, In summary, m j µc jk µ, µ µ. a if S is a proto-ring and µ is a measure on S, then µ is finitely additive and subtractive propositions 5.1 and 4.1, b if S is a semi-ring and µ is a measure on S, then, in addition to being finitely additive and subtractive as in a, the measure µ is also monotone and countably subadditive propositions 4.2c and 5.2, c if S is a semi-ring and µ is a finitely additive and countably subadditive proto-measure on S, then µ is a measure proposition 4.3, d every σ-algebra is a semi-ring proposition 3.2, b and c also hold if S is a σ-algebra. 6 Outer measures A function µ from PX into [0, ] is an outer measure if it is a proto-measure, monotone, and countably subadditive. Suppose µ is an outer measure and A is a subset of X. The set A is µ- measurable if µb = µb A + µb A c for every subset B of X. Since µ is a proto-measure and countably subadditive, it always holds that µb µb A + µb A c. 6

7 The opposite inequality clearly holds if µb =. It follows that A is µ- measurable if and only if µb µb A + µb A c for every B such that µb <. Let M µ be the set of all µ-measurable subsets of X. We will show that M µ is a σ-algebra and µ Mµ the restriction of µ to M µ is a measure on M µ. Proposition 6.1. If µ is an outer measure, B is a subset of X, and A 1, A 2,..., A n are disjoint sets in M µ, then µb = µb + µ B Proof. We will use induction on n. The base case follows from the µ-measurability of A 1. Consider the inductive case. Since A n+1 is µ-measurable, µ B n A c j = µ B n Since A 1, A 2,..., A n, A n+1 are disjoint, µ B B n A c j n A c j A c j A n+1 + µ B n A c j A n+1 = B A n+1, It follows from the inductive hypothesis that µb = µb + µ B. n A c j A c n+1. n+1 = µb A n+1 + µ B n A c j A c j. n+1 n+1 = µb + µ B Theorem 6.2. If µ is an outer measure, then M µ is a σ-algebra. Proof. By proposition 2.1, it is sufficient to show that M µ is a proto-ring, closed under complements, closed under finite unions, and closed under disjoint countable unions. Proto-ring. For every subset B of X, µb + µb c = µ + µb = µb, is µ-measurable. Closed under complements. Suppose A is µ-measurable. For every subset B of X, µb A c + µb A c c = µb A c + µb A = µb. A c j. 7

8 Closed under finite unions. Suppose A 1, A 2 are µ-measurable. For every subset B of X, µ B A 1 A 2 + µ B A 1 A 2 c = µ B A 1 A 2 + µb A c 1 A c 2 = µ B A 1 A 2 A 1 + µ B A 1 A 2 A c 1 + µb A c 1 A c 2 = µb A 1 + µb A c 1 A 2 + µb A c 1 A c 2 = µb A 1 + µb A c 1 = µb. Closed under disjoint countable unions. Suppose is a sequence of disjoint sets in M µ, and B is a subset of X such that µb <. For every n, by proposition 6.1 and monotonicity, n µb = µb + µ B µb µb + µ B µb + µ B By countable subadditivity, µb µ B = µ B A c j A c j, c., µb µ B + µ B c. Theorem 6.3. If µ is an outer measure, then µ Mµ is a measure on M µ. Proof. Suppose is a sequence of disjoint sets in M µ. Since M µ is a σ- algebra, we have M µ. For every n, by proposition 6.1, n µ = µ A k + µ µa k, µ µ. The opposite inequality holds by countable subadditivity. A c k 8

9 7 Constructing an outer measure If µ is a function from a subset S of PX into [0, ], let µ be the function from PX into [0, ] defined by { µ A = inf µ : is a sequence in S such that A with the understanding that inf =. }, Theorem 7.1. If S is a proto-ring and µ is a proto-measure on S, then µ is an outer measure and µ µ on S. Proof. Proto-measure. Let be the sequence in S defined by = for every j, then, Monotone. If A B then 0 µ µa i = 0. { } µ : is a sequence in S such that B i=1 { µ : is a sequence in S such that A }, µ A µ B. Countably subadditive. Suppose is a sequence in PX. Countable subadditivity clearly holds if µ =, so suppose µ < and ɛ > 0. For every j, there exists a sequence k,2,... in S such that k and µk < µ + ɛ/2 j. Choose a bijection f from N onto N N and let B j be the sequence defined by B j = A fj, then B j is a sequence in S such that B j, µ µb j. By proposition 11.1, µb j = µk, µ µk µ + ɛ/2 j = µ + ɛ. 9

10 Inequality. Suppose A S. Let be the sequence defined by A 1 = A and = for every j 2. We then have that is a sequence in S such that A, µ A µ = µa. 8 Extending a measure on a semi-ring We have shown that if S is a proto-ring and µ is a proto-measure on S, then a µ is an outer measure theorem 7.1, b M µ is a σ-algebra theorem 6.2, c µ Mµ is a measure on M µ theorem 6.3, d µ µ on S theorem 7.1. In this section, we will show that if i S is a semi-ring, ii µ is a finitely additive, countably subadditive and monotone proto-measure on S, then, in addition to a-d, the measure µ Mµ is an extension of µ. In other words, S M µ and µ = µ on S. Note that S and µ satisfy i-ii if and only if S is a semi-ring and µ is a measure on S. Theorem 8.1. If S is a semi-ring and µ is a finitely additive proto-measure on S, then S M µ. Proof. Suppose that A S, that B is a subset of X such that µ B <, and that ɛ > 0. There exists a sequence B j in S such that B B j and We have µb j < µ B + ɛ. B A B j A = B j A and B j A S for every j, µ B A µb j A. We also have B A c B j A c = B j A. 10

11 For every j, there exist disjoint sets C j1, C j2,..., C jmj in S such that It follows that B j A = m j C jk. C jk j =1,2,...,2,...,m j is a sequence of sets in S whose union contains B A c, Combining these inequalities, µ B A c m j µc jk. m j µ B A + µ B A c µb j A + µc jk = m j µb j A + µc jk. For every j, we have that B j A, C j1, C j2,..., C jmj union is B j, are disjoint sets in S whose m j µb j A + µc jk = µb j, µ B A + µ B A c µb j < µ B + ɛ. Theorem 8.2. If S is a semi-ring and µ is a countably subadditive and monotone function on S, then µ µ on S. Proof. Suppose A S and is a sequence in S such that A. We have A = A = A and A S for every j, µa = µ A µa µ, µa µ A. 11

12 9 Uniqueness of the extension We will show that the extension of the previous section is unique if the measure is σ-finite. Theorem 9.1. Suppose S is a semi-ring and µ is a σ-finite measure on S. If ν is a measure on M µ such that ν = µ on S, then ν = µ on M µ. Proof. Suppose A M µ. We will show that νa = µ A. Througout this proof, remember that M µ is a σ-algebra, µ Mµ is a measure, S M µ and µ = µ = ν on S. Step 1. For every sequence in S such that A, we have νa ν ν = µ, νa µ A. Step 2. For the opposite inequality, first suppose that µ A < and ɛ > 0. There exists a sequence in S such that A and By monotonicity, µ < µ A + ɛ. Let B 1 = A 1, and for every j 2 let µ A µ. B j = For every j, there exist disjoint sets C j1, C j2,..., C jmj in S such that B j = mj C jk. It follows that C jk j =1,2,...,2,...,m j j 1 A k. is a sequence of disjoint sets in S whose union is, m j m j µ = µ C jk = νc jk = ν, µ A ν. We have shown in step 1 that ν µ on M µ, ν = νa + ν A νa + µ A, 12

13 µ A νa + µ A. Since µ A <, µ A = µ µ A µ µ A < ɛ, µ A < νa + ɛ. Step 3. Now suppose µ A =. There exists a sequence C j of disjoint sets in S such that µc j < for every j and C j = X. For every j, µ A C j µ C j = µc j <, µ A C j = νa C j by step 2. It follows that µ A = µ A C j = µ A C j = νa C j = νa. 10 Lebesgue-Stieltjes measures We will use the results of the previous sections to construct a σ-algebra and a measure from a real-valued function on R d satisfying certain nonnegativity and continuity conditions. A subset A of R d is a rectangle if A = or A = d a k, b k ] with a k < b k for every k. Let S d be the set of rectangles in R d. If a, b R, let a b and a b be the minimum and maximum of a, b respectively. Theorem S d is a semi-ring. Proof. Proto-ring. S d by definition. Closed under finite intersections. If A 1 = d a 1k, b 1k ] and A 2 = d a 2k, b 2k ] are nonempty rectangles, then A 1 A 2 = = a 1k, b 1k ] a 2k, b 2k ] a 1k a 2k, b 1k b 2k ]. Partially closed under differences. Suppose A 1 = d a 1k, b 1k ] and A 2 = d a 2k, b 2k ] are nonempty rectangles. Let Y be the set of all {0, 1}-valued sequences of length d, excluding 1, 1,..., 1, i.e. Y has 2 d 1 elements. For every y = y 1, y 2,..., y d Y and k {1, 2,..., d}, let { a1k, b 1k ] a 2k, b 2k ] if y k = 1 B k y = a 1k, b 1k ] a 2k, b 2k ] if y k = 0 { a1k a 2k, b 1k b 2k ] if y k = 1 = a 1k, b 1k a 2k ] a 1k b 2k, b 1k ] if y k = 0, 13

14 b 12 A 1 B0, 0 B1, 0 b 22 A 2 B0, 1 a 22 B0, 0 B1, 0 a 12 a 11 a 21 b 11 b 21 Figure 10.1: An example of the decomposition in the proof of theorem and let see figure We have By = B k y A 1 A 2 = By, y Y which is a disjoint union of rectangles. If A = d a k, b k ] is a nonempty rectangle, let V A = { x 1, x 2,..., x d R d : x k = a k or x k = b k for every k }, and for every x = x 1, x 2,..., x d V A let { 1 if xk = a k for an odd number of k sgn A x = +1 if x k = a k for an even number of k. Suppose F is a function from R d into R that is i nondecreasing, i.e. for every nonempty rectangle A we have sgn A xf x 0, x V A ii continuous from above, i.e. for every x R d and ɛ > 0 there exists δ > 0 such that if h d [0, δ then F x + h F x < ɛ. 14

15 Let µ F be the function from S d into [0, ] defined by sgn A xf x if A µ F A = x V A 0 if A =. For example, if d = 2 and A = a 1, b 1 ] a 2, b 2 ] is a nonempty rectangle, then Proposition If µ F A = F b 1, b 2 F a 1, b 2 F b 1, a 2 + F a 1, a 2. i A = d a k, b k ] is a nonempty rectangle, ii k 0 {1, 2,..., d}, iii c a k0, b k0, iv for every k, B k = { ak, b k ] if k k 0 a k, c] if k = k 0, C k = { ak, b k ] if k k 0 c, b k ] if k = k 0, v B = d B k and C = d C k, then µ F A = µ F B + µ F C. Proof. Let V B = {x 1, x 2,..., x d V B : x k0 = a k } V B = {x 1, x 2,..., x d V B : x k0 = c} V C = {x 1, x 2,..., x d V C : x k0 = b k } V C = {x 1, x 2,..., x d V C : x k0 = c}. The sets V B, V C are disjoint and their union is V A. If x V B then sgn Bx = sgn A x, and if x V C then sgn Cx = sgn A x. The sets V B, V C are in fact equal, so let V = V B = V C see figure If x V then sgn C x = sgn B x, because c is the right-endpoint of B k0 and the left-endpoint of C k0. It follows that µ F B = sgn A xf x + sgn B xf x x V x V B and µ F C = sgn A xf x x V C µ F B + µ F C = sgn B xf x, x V sgn A xf x = µ F A. x V B V C 15

16 b 2 V B V V C A B C a 2 V B V V C a 1 c b 1 Figure 10.2: An example of the decomposition in the proof of proposition Proposition Suppose A = d a k, b k ] is a nonempty rectangle and ɛ > 0. a There exists δ > 0 such that δ < b k a k for every k and µ F A < µ F k + δ, b k ] + ɛ. a b There exists δ > 0 such that µ F k, b k + δ] < µ F A + ɛ. a Proof. a Since F is continuous from above, for every x V A there exists δ x > 0 such that δ x < b k a k for every k and if h d [0, δ x then Let F x + h F x < ɛ/2 d. δ = 1 2 min{δ x : x V A}, B = a k + δ, b k ]. For every x = x 1, x 2,..., x d V A, let h x be the element of R d whose k-th entry is 0 if x k = b k and δ if x k = a k, h x d [0, δ x. The function x x + h x is a bijection between V A and V B, and sgn A x = sgn B x + h x, 16

17 µ F B = sgn B yf y y V B = sgn B x + h x F x + h x x V A = sgn A xf x + h x. x V A It follows that µ F A µ F B = b Similar to part a. x V A x V A < = ɛ. x V A sgn A xf x F x + h x F x + hx F x ɛ/2 d Theorem µ F is a σ-finite measure on S d. Proof. σ-finite. For every j, let = d j, j], then is a sequence in S d. Furthermore, µ F < for every j and = R d. Proto-measure. We have µ F = 0 by definition. Finitely additive. Suppose A = d a k, b k ] is a nonempty rectangle and A 1 = a 1k, b 1k ], A 2 = a 2k, b 2k ],..., A n = a nk, b nk ] are disjoint nonempty rectangles such that A = n. For every k {1, 2,..., d}, let c k1, c k2,..., c k,mk be the distinct elements in the set labelled such that {a 1k, b 1k, a 2k, b 2k,..., a nk, b nk }, c k1 < c k2 < < c k,mk. Since a 1k < b 1k we have m k 2, and since A = n we have a k = c 1k and b k = c k,mk. Let Y be the set of all sequences y = y 1, y 2,..., y d such that y k {1, 2,..., m k 1} for every k, Y has m 1 1m 2 1 m k 1 elements. For every y = y 1, y 2,..., y d Y, let By = c k,yk, c k,yk +1]. see figure After multiple applications of proposition 10.2, we find that both µ F A and n µ F are equal to y Y µ F By. 17

18 c 24 B1, 3 B2, 3 B3, 3 c 23 B1, 2 B2, 2 B3, 2 c 22 B1, 1 B2, 1 B3, 1 c 21 c 11 c 12 c 13 c 14 Figure 10.3: An example of the decomposition in the proof of theorem 10.4 Countably subadditive. Suppose that A = d a k, b k ] is a nonempty rectangle, that A 1 = a 1k, b 1k ], A 2 = a 2k, b 2k ],... are nonempty rectangles such that A =, and that ɛ > 0. By proposition 10.3, for every j there exists δ j > 0 such that µ F jk, b jk + δ j ] < µ F + ɛ/2 a j+1, and there exists δ > 0 such that δ < b k a k for every k and µ F A < µ F k + δ, b k ] + ɛ/2. a We have and [a k + δ, b k ] A = = a k, b k ] = A, a jk, b jk ] [a k + δ, b k ] a jk, b jk + δ j, a jk, b jk + δ j. Since the left-hand side is compact and the right-hand side is an open cover, there exists n such that n [a k + δ, b k ] a jk, b jk + δ j, 18

19 a k + δ, b k ] By proposition 4.2b, µ F k + δ, b k ] a n a jk, b jk + δ j ]. µ F a jk, b jk + δ j ]. It follows that µ F A < µ F k + δ, b k ] + ɛ/2 a a jk, b jk + δ j ] + ɛ/2 µ F < µ F + ɛ/2 j+1 + ɛ/2 = µ F + ɛ/2 j+1 + ɛ/2 µ F + ɛ. Since S d is a semi-ring and µ F is a σ-finite measure on S d, a µ F is an outer measure theorem 7.1, b M µ F is a σ-algebra theorem 6.2, c µ F M µ F is a measure on M µ F theorem 6.3, d µ F M µ F is an extension of µ F theorem 8.1 and 8.2, e this extension is unique theorem 9.1. The measure µ F M µ F is the Lebesgue-Stieltjes measure with respect to F. 11 Appendix: a result on double series The following result on double series was used in the proof of theorem 7.1. Proposition If a jk is a double sequence in [0, ] and f is a bijection from N onto N N, then a fj = a jk. 19

20 Proof. For every j, n, let s n j = t n = u n = v n = a jk s j = s n j = a fj. a jk a jk s j = lim n sn j = t = lim n tn = a jk a jk Note that, for every n, u n t n t. Step 1. We will first show that u n t. Step 1a. Suppose that s j0 = for some j 0 which implies that t =, and that x <. There exists n 0 such that if n n 0 then If n max{j 0, n 0 } then u n = x < s n j 0. j j 0 a jk + s n j 0 > x. Step 1b. Now suppose that s j < for every j, and that x < y < t. There exists m 0 such that y < t m0 t. For every j = 1, 2,..., m 0, there exists n j such that if n n j then If n max{m 0, n 1, n 2,..., n m0 } then u n = s n j m 0 s j y x 2 j < s n j s j. m 0 s n j > s j y x 2 j m 0 = t m0 y x 2 j > y y x 2 j = x. Step 2. We can now show that v n t. Suppose x < t. There exists m 0 such that x < u m0 t. Since f is a bijection, there exists n 0 such that {1, 2,..., m 0 } {1, 2,..., m 0 } f {1, 2,..., n 0 }. 20

21 Suppose n n 0. There exists m such that f {1, 2,..., n} {1, 2,..., m} {1, 2,..., m}, x < u m0 v n0 v n u m t. References [B86] P. Billingsley, Probability and Measure, 2nd ed., John Wiley & Sons, New York, Chichester, Brisbane, Toronto, Singapore, [C80] D.L. Cohn, Measure Theory, Birkhäuser, Boston, Basel, Stuttgart, [F99] G.B. Folland, Real Analysis: Modern Techniques and Their Applications, 2nd ed., John Wiley & Sons, New York, Chichester, Weinheim, Brisbane, Singapore, Toronto, [S09] S. Sawyer, Measures on semi-rings in R 1 and R k, wustl.edu/~sawyer/handouts/measuressemirings.pdf, 2009, accessed 7 October Copyright This work is licensed under a Creative Commons Attribution 4.0 International License. 21

Chapter 4. Measure Theory. 1. Measure Spaces

Chapter 4. Measure Theory. 1. Measure Spaces Chapter 4. Measure Theory 1. Measure Spaces Let X be a nonempty set. A collection S of subsets of X is said to be an algebra on X if S has the following properties: 1. X S; 2. if A S, then A c S; 3. if

More information

The Caratheodory Construction of Measures

The Caratheodory Construction of Measures Chapter 5 The Caratheodory Construction of Measures Recall how our construction of Lebesgue measure in Chapter 2 proceeded from an initial notion of the size of a very restricted class of subsets of R,

More information

1.4 Outer measures 10 CHAPTER 1. MEASURE

1.4 Outer measures 10 CHAPTER 1. MEASURE 10 CHAPTER 1. MEASURE 1.3.6. ( Almost everywhere and null sets If (X, A, µ is a measure space, then a set in A is called a null set (or µ-null if its measure is 0. Clearly a countable union of null sets

More information

Measures. 1 Introduction. These preliminary lecture notes are partly based on textbooks by Athreya and Lahiri, Capinski and Kopp, and Folland.

Measures. 1 Introduction. These preliminary lecture notes are partly based on textbooks by Athreya and Lahiri, Capinski and Kopp, and Folland. Measures These preliminary lecture notes are partly based on textbooks by Athreya and Lahiri, Capinski and Kopp, and Folland. 1 Introduction Our motivation for studying measure theory is to lay a foundation

More information

Chapter 1. Measure Spaces. 1.1 Algebras and σ algebras of sets Notation and preliminaries

Chapter 1. Measure Spaces. 1.1 Algebras and σ algebras of sets Notation and preliminaries Chapter 1 Measure Spaces 1.1 Algebras and σ algebras of sets 1.1.1 Notation and preliminaries We shall denote by X a nonempty set, by P(X) the set of all parts (i.e., subsets) of X, and by the empty set.

More information

Annalee Gomm Math 714: Assignment #2

Annalee Gomm Math 714: Assignment #2 Annalee Gomm Math 714: Assignment #2 3.32. Verify that if A M, λ(a = 0, and B A, then B M and λ(b = 0. Suppose that A M with λ(a = 0, and let B be any subset of A. By the nonnegativity and monotonicity

More information

II - REAL ANALYSIS. This property gives us a way to extend the notion of content to finite unions of rectangles: we define

II - REAL ANALYSIS. This property gives us a way to extend the notion of content to finite unions of rectangles: we define 1 Measures 1.1 Jordan content in R N II - REAL ANALYSIS Let I be an interval in R. Then its 1-content is defined as c 1 (I) := b a if I is bounded with endpoints a, b. If I is unbounded, we define c 1

More information

THEOREMS, ETC., FOR MATH 516

THEOREMS, ETC., FOR MATH 516 THEOREMS, ETC., FOR MATH 516 Results labeled Theorem Ea.b.c (or Proposition Ea.b.c, etc.) refer to Theorem c from section a.b of Evans book (Partial Differential Equations). Proposition 1 (=Proposition

More information

First In-Class Exam Solutions Math 410, Professor David Levermore Monday, 1 October 2018

First In-Class Exam Solutions Math 410, Professor David Levermore Monday, 1 October 2018 First In-Class Exam Solutions Math 40, Professor David Levermore Monday, October 208. [0] Let {b k } k N be a sequence in R and let A be a subset of R. Write the negations of the following assertions.

More information

µ (X) := inf l(i k ) where X k=1 I k, I k an open interval Notice that is a map from subsets of R to non-negative number together with infinity

µ (X) := inf l(i k ) where X k=1 I k, I k an open interval Notice that is a map from subsets of R to non-negative number together with infinity A crash course in Lebesgue measure theory, Math 317, Intro to Analysis II These lecture notes are inspired by the third edition of Royden s Real analysis. The Jordan content is an attempt to extend the

More information

Solutions to Tutorial 1 (Week 2)

Solutions to Tutorial 1 (Week 2) THE UNIVERSITY OF SYDNEY SCHOOL OF MATHEMATICS AND STATISTICS Solutions to Tutorial 1 (Week 2 MATH3969: Measure Theory and Fourier Analysis (Advanced Semester 2, 2017 Web Page: http://sydney.edu.au/science/maths/u/ug/sm/math3969/

More information

(2) E M = E C = X\E M

(2) E M = E C = X\E M 10 RICHARD B. MELROSE 2. Measures and σ-algebras An outer measure such as µ is a rather crude object since, even if the A i are disjoint, there is generally strict inequality in (1.14). It turns out to

More information

Lebesgue measure on R is just one of many important measures in mathematics. In these notes we introduce the general framework for measures.

Lebesgue measure on R is just one of many important measures in mathematics. In these notes we introduce the general framework for measures. Measures In General Lebesgue measure on R is just one of many important measures in mathematics. In these notes we introduce the general framework for measures. Definition: σ-algebra Let X be a set. A

More information

Final. due May 8, 2012

Final. due May 8, 2012 Final due May 8, 2012 Write your solutions clearly in complete sentences. All notation used must be properly introduced. Your arguments besides being correct should be also complete. Pay close attention

More information

Measures. Chapter Some prerequisites. 1.2 Introduction

Measures. Chapter Some prerequisites. 1.2 Introduction Lecture notes Course Analysis for PhD students Uppsala University, Spring 2018 Rostyslav Kozhan Chapter 1 Measures 1.1 Some prerequisites I will follow closely the textbook Real analysis: Modern Techniques

More information

Spring 2014 Advanced Probability Overview. Lecture Notes Set 1: Course Overview, σ-fields, and Measures

Spring 2014 Advanced Probability Overview. Lecture Notes Set 1: Course Overview, σ-fields, and Measures 36-752 Spring 2014 Advanced Probability Overview Lecture Notes Set 1: Course Overview, σ-fields, and Measures Instructor: Jing Lei Associated reading: Sec 1.1-1.4 of Ash and Doléans-Dade; Sec 1.1 and A.1

More information

Construction of a general measure structure

Construction of a general measure structure Chapter 4 Construction of a general measure structure We turn to the development of general measure theory. The ingredients are a set describing the universe of points, a class of measurable subsets along

More information

MATHS 730 FC Lecture Notes March 5, Introduction

MATHS 730 FC Lecture Notes March 5, Introduction 1 INTRODUCTION MATHS 730 FC Lecture Notes March 5, 2014 1 Introduction Definition. If A, B are sets and there exists a bijection A B, they have the same cardinality, which we write as A, #A. If there exists

More information

MATH & MATH FUNCTIONS OF A REAL VARIABLE EXERCISES FALL 2015 & SPRING Scientia Imperii Decus et Tutamen 1

MATH & MATH FUNCTIONS OF A REAL VARIABLE EXERCISES FALL 2015 & SPRING Scientia Imperii Decus et Tutamen 1 MATH 5310.001 & MATH 5320.001 FUNCTIONS OF A REAL VARIABLE EXERCISES FALL 2015 & SPRING 2016 Scientia Imperii Decus et Tutamen 1 Robert R. Kallman University of North Texas Department of Mathematics 1155

More information

arxiv: v1 [math.fa] 14 Jul 2018

arxiv: v1 [math.fa] 14 Jul 2018 Construction of Regular Non-Atomic arxiv:180705437v1 [mathfa] 14 Jul 2018 Strictly-Positive Measures in Second-Countable Locally Compact Non-Atomic Hausdorff Spaces Abstract Jason Bentley Department of

More information

Introduction to Hausdorff Measure and Dimension

Introduction to Hausdorff Measure and Dimension Introduction to Hausdorff Measure and Dimension Dynamics Learning Seminar, Liverpool) Poj Lertchoosakul 28 September 2012 1 Definition of Hausdorff Measure and Dimension Let X, d) be a metric space, let

More information

Measures and Measure Spaces

Measures and Measure Spaces Chapter 2 Measures and Measure Spaces In summarizing the flaws of the Riemann integral we can focus on two main points: 1) Many nice functions are not Riemann integrable. 2) The Riemann integral does not

More information

CHAPTER 6. Differentiation

CHAPTER 6. Differentiation CHPTER 6 Differentiation The generalization from elementary calculus of differentiation in measure theory is less obvious than that of integration, and the methods of treating it are somewhat involved.

More information

+ 2x sin x. f(b i ) f(a i ) < ɛ. i=1. i=1

+ 2x sin x. f(b i ) f(a i ) < ɛ. i=1. i=1 Appendix To understand weak derivatives and distributional derivatives in the simplest context of functions of a single variable, we describe without proof some results from real analysis (see [7] and

More information

x 0 + f(x), exist as extended real numbers. Show that f is upper semicontinuous This shows ( ɛ, ɛ) B α. Thus

x 0 + f(x), exist as extended real numbers. Show that f is upper semicontinuous This shows ( ɛ, ɛ) B α. Thus Homework 3 Solutions, Real Analysis I, Fall, 2010. (9) Let f : (, ) [, ] be a function whose restriction to (, 0) (0, ) is continuous. Assume the one-sided limits p = lim x 0 f(x), q = lim x 0 + f(x) exist

More information

THEOREMS, ETC., FOR MATH 515

THEOREMS, ETC., FOR MATH 515 THEOREMS, ETC., FOR MATH 515 Proposition 1 (=comment on page 17). If A is an algebra, then any finite union or finite intersection of sets in A is also in A. Proposition 2 (=Proposition 1.1). For every

More information

Measure and Integration: Concepts, Examples and Exercises. INDER K. RANA Indian Institute of Technology Bombay India

Measure and Integration: Concepts, Examples and Exercises. INDER K. RANA Indian Institute of Technology Bombay India Measure and Integration: Concepts, Examples and Exercises INDER K. RANA Indian Institute of Technology Bombay India Department of Mathematics, Indian Institute of Technology, Bombay, Powai, Mumbai 400076,

More information

Lebesgue Measure on R n

Lebesgue Measure on R n CHAPTER 2 Lebesgue Measure on R n Our goal is to construct a notion of the volume, or Lebesgue measure, of rather general subsets of R n that reduces to the usual volume of elementary geometrical sets

More information

Lebesgue measure and integration

Lebesgue measure and integration Chapter 4 Lebesgue measure and integration If you look back at what you have learned in your earlier mathematics courses, you will definitely recall a lot about area and volume from the simple formulas

More information

Lebesgue Measure. Dung Le 1

Lebesgue Measure. Dung Le 1 Lebesgue Measure Dung Le 1 1 Introduction How do we measure the size of a set in IR? Let s start with the simplest ones: intervals. Obviously, the natural candidate for a measure of an interval is its

More information

Partial Solutions to Folland s Real Analysis: Part I

Partial Solutions to Folland s Real Analysis: Part I Partial Solutions to Folland s Real Analysis: Part I (Assigned Problems from MAT1000: Real Analysis I) Jonathan Mostovoy - 1002142665 University of Toronto January 20, 2018 Contents 1 Chapter 1 3 1.1 Folland

More information

An extended version of the Carathéodory extension Theorem

An extended version of the Carathéodory extension Theorem An extended version of the Carathéodory extension Theorem Alexandre G. Patriota Departamento de Estatística, Universidade de São Paulo, São Paulo/SP, 05508-090, Brazil Abstract In this paper, we show that

More information

MATH MEASURE THEORY AND FOURIER ANALYSIS. Contents

MATH MEASURE THEORY AND FOURIER ANALYSIS. Contents MATH 3969 - MEASURE THEORY AND FOURIER ANALYSIS ANDREW TULLOCH Contents 1. Measure Theory 2 1.1. Properties of Measures 3 1.2. Constructing σ-algebras and measures 3 1.3. Properties of the Lebesgue measure

More information

Math 4121 Spring 2012 Weaver. Measure Theory. 1. σ-algebras

Math 4121 Spring 2012 Weaver. Measure Theory. 1. σ-algebras Math 4121 Spring 2012 Weaver Measure Theory 1. σ-algebras A measure is a function which gauges the size of subsets of a given set. In general we do not ask that a measure evaluate the size of every subset,

More information

Measure theory and countable Borel equivalence relations

Measure theory and countable Borel equivalence relations Measure theory and countable Borel equivalence relations Benjamin Miller Kurt Gödel Institute for Mathematical Logic Universität Wien Winter Semester, 2016 Introduction These are the notes accompanying

More information

Section Signed Measures: The Hahn and Jordan Decompositions

Section Signed Measures: The Hahn and Jordan Decompositions 17.2. Signed Measures 1 Section 17.2. Signed Measures: The Hahn and Jordan Decompositions Note. If measure space (X, M) admits measures µ 1 and µ 2, then for any α,β R where α 0,β 0, µ 3 = αµ 1 + βµ 2

More information

Integration on Measure Spaces

Integration on Measure Spaces Chapter 3 Integration on Measure Spaces In this chapter we introduce the general notion of a measure on a space X, define the class of measurable functions, and define the integral, first on a class of

More information

Reminder Notes for the Course on Measures on Topological Spaces

Reminder Notes for the Course on Measures on Topological Spaces Reminder Notes for the Course on Measures on Topological Spaces T. C. Dorlas Dublin Institute for Advanced Studies School of Theoretical Physics 10 Burlington Road, Dublin 4, Ireland. Email: dorlas@stp.dias.ie

More information

INTRODUCTION TO MEASURE THEORY AND LEBESGUE INTEGRATION

INTRODUCTION TO MEASURE THEORY AND LEBESGUE INTEGRATION 1 INTRODUCTION TO MEASURE THEORY AND LEBESGUE INTEGRATION Eduard EMELYANOV Ankara TURKEY 2007 2 FOREWORD This book grew out of a one-semester course for graduate students that the author have taught at

More information

Measure Theory. John K. Hunter. Department of Mathematics, University of California at Davis

Measure Theory. John K. Hunter. Department of Mathematics, University of California at Davis Measure Theory John K. Hunter Department of Mathematics, University of California at Davis Abstract. These are some brief notes on measure theory, concentrating on Lebesgue measure on R n. Some missing

More information

MTH 404: Measure and Integration

MTH 404: Measure and Integration MTH 404: Measure and Integration Semester 2, 2012-2013 Dr. Prahlad Vaidyanathan Contents I. Introduction....................................... 3 1. Motivation................................... 3 2. The

More information

Moreover, µ is monotone, that is for any A B which are both elements of A we have

Moreover, µ is monotone, that is for any A B which are both elements of A we have FRACTALS Contents. Algebras, sigma algebras, and measures 2.. Carathéodory s outer measures 5.2. Completeness 6.3. Homework: Measure theory basics 7 2. Completion of a measure, creating a measure from

More information

Measure and integration

Measure and integration Chapter 5 Measure and integration In calculus you have learned how to calculate the size of different kinds of sets: the length of a curve, the area of a region or a surface, the volume or mass of a solid.

More information

MA359 Measure Theory

MA359 Measure Theory A359 easure Theory Thomas Reddington Usman Qureshi April 8, 204 Contents Real Line 3. Cantor set.................................................. 5 2 General easures 2 2. Product spaces...............................................

More information

Indeed, if we want m to be compatible with taking limits, it should be countably additive, meaning that ( )

Indeed, if we want m to be compatible with taking limits, it should be countably additive, meaning that ( ) Lebesgue Measure The idea of the Lebesgue integral is to first define a measure on subsets of R. That is, we wish to assign a number m(s to each subset S of R, representing the total length that S takes

More information

MATH 202B - Problem Set 5

MATH 202B - Problem Set 5 MATH 202B - Problem Set 5 Walid Krichene (23265217) March 6, 2013 (5.1) Show that there exists a continuous function F : [0, 1] R which is monotonic on no interval of positive length. proof We know there

More information

Real Analysis Math 131AH Rudin, Chapter #1. Dominique Abdi

Real Analysis Math 131AH Rudin, Chapter #1. Dominique Abdi Real Analysis Math 3AH Rudin, Chapter # Dominique Abdi.. If r is rational (r 0) and x is irrational, prove that r + x and rx are irrational. Solution. Assume the contrary, that r+x and rx are rational.

More information

Another Riesz Representation Theorem

Another Riesz Representation Theorem Another Riesz Representation Theorem In these notes we prove (one version of) a theorem known as the Riesz Representation Theorem. Some people also call it the Riesz Markov Theorem. It expresses positive

More information

+ = x. ± if x > 0. if x < 0, x

+ = x. ± if x > 0. if x < 0, x 2 Set Functions Notation Let R R {, + } and R + {x R : x 0} {+ }. Here + and are symbols satisfying obvious conditions: for any real number x R : < x < +, (± + (± x + (± (± + x ±, (± (± + and (± (, ± if

More information

Lebesgue Integration: A non-rigorous introduction. What is wrong with Riemann integration?

Lebesgue Integration: A non-rigorous introduction. What is wrong with Riemann integration? Lebesgue Integration: A non-rigorous introduction What is wrong with Riemann integration? xample. Let f(x) = { 0 for x Q 1 for x / Q. The upper integral is 1, while the lower integral is 0. Yet, the function

More information

M17 MAT25-21 HOMEWORK 6

M17 MAT25-21 HOMEWORK 6 M17 MAT25-21 HOMEWORK 6 DUE 10:00AM WEDNESDAY SEPTEMBER 13TH 1. To Hand In Double Series. The exercises in this section will guide you to complete the proof of the following theorem: Theorem 1: Absolute

More information

Review of measure theory

Review of measure theory 209: Honors nalysis in R n Review of measure theory 1 Outer measure, measure, measurable sets Definition 1 Let X be a set. nonempty family R of subsets of X is a ring if, B R B R and, B R B R hold. bove,

More information

3 (Due ). Let A X consist of points (x, y) such that either x or y is a rational number. Is A measurable? What is its Lebesgue measure?

3 (Due ). Let A X consist of points (x, y) such that either x or y is a rational number. Is A measurable? What is its Lebesgue measure? MA 645-4A (Real Analysis), Dr. Chernov Homework assignment 1 (Due ). Show that the open disk x 2 + y 2 < 1 is a countable union of planar elementary sets. Show that the closed disk x 2 + y 2 1 is a countable

More information

DRAFT MAA6616 COURSE NOTES FALL 2015

DRAFT MAA6616 COURSE NOTES FALL 2015 Contents 1. σ-algebras 2 1.1. The Borel σ-algebra over R 5 1.2. Product σ-algebras 7 2. Measures 8 3. Outer measures and the Caratheodory Extension Theorem 11 4. Construction of Lebesgue measure 15 5.

More information

Math 172 HW 1 Solutions

Math 172 HW 1 Solutions Math 172 HW 1 Solutions Joey Zou April 15, 2017 Problem 1: Prove that the Cantor set C constructed in the text is totally disconnected and perfect. In other words, given two distinct points x, y C, there

More information

Solutions to Tutorial 11 (Week 12)

Solutions to Tutorial 11 (Week 12) THE UIVERSITY OF SYDEY SCHOOL OF MATHEMATICS AD STATISTICS Solutions to Tutorial 11 (Week 12) MATH3969: Measure Theory and Fourier Analysis (Advanced) Semester 2, 2017 Web Page: http://sydney.edu.au/science/maths/u/ug/sm/math3969/

More information

Product measures, Tonelli s and Fubini s theorems For use in MAT4410, autumn 2017 Nadia S. Larsen. 17 November 2017.

Product measures, Tonelli s and Fubini s theorems For use in MAT4410, autumn 2017 Nadia S. Larsen. 17 November 2017. Product measures, Tonelli s and Fubini s theorems For use in MAT4410, autumn 017 Nadia S. Larsen 17 November 017. 1. Construction of the product measure The purpose of these notes is to prove the main

More information

MATS113 ADVANCED MEASURE THEORY SPRING 2016

MATS113 ADVANCED MEASURE THEORY SPRING 2016 MATS113 ADVANCED MEASURE THEORY SPRING 2016 Foreword These are the lecture notes for the course Advanced Measure Theory given at the University of Jyväskylä in the Spring of 2016. The lecture notes can

More information

l(y j ) = 0 for all y j (1)

l(y j ) = 0 for all y j (1) Problem 1. The closed linear span of a subset {y j } of a normed vector space is defined as the intersection of all closed subspaces containing all y j and thus the smallest such subspace. 1 Show that

More information

REAL ANALYSIS LECTURE NOTES: 1.4 OUTER MEASURE

REAL ANALYSIS LECTURE NOTES: 1.4 OUTER MEASURE REAL ANALYSIS LECTURE NOTES: 1.4 OUTER MEASURE CHRISTOPHER HEIL 1.4.1 Introduction We will expand on Section 1.4 of Folland s text, which covers abstract outer measures also called exterior measures).

More information

Problem set 1, Real Analysis I, Spring, 2015.

Problem set 1, Real Analysis I, Spring, 2015. Problem set 1, Real Analysis I, Spring, 015. (1) Let f n : D R be a sequence of functions with domain D R n. Recall that f n f uniformly if and only if for all ɛ > 0, there is an N = N(ɛ) so that if n

More information

Lebesgue Measure on R n

Lebesgue Measure on R n 8 CHAPTER 2 Lebesgue Measure on R n Our goal is to construct a notion of the volume, or Lebesgue measure, of rather general subsets of R n that reduces to the usual volume of elementary geometrical sets

More information

Analysis Comprehensive Exam Questions Fall F(x) = 1 x. f(t)dt. t 1 2. tf 2 (t)dt. and g(t, x) = 2 t. 2 t

Analysis Comprehensive Exam Questions Fall F(x) = 1 x. f(t)dt. t 1 2. tf 2 (t)dt. and g(t, x) = 2 t. 2 t Analysis Comprehensive Exam Questions Fall 2. Let f L 2 (, ) be given. (a) Prove that ( x 2 f(t) dt) 2 x x t f(t) 2 dt. (b) Given part (a), prove that F L 2 (, ) 2 f L 2 (, ), where F(x) = x (a) Using

More information

Dynkin (λ-) and π-systems; monotone classes of sets, and of functions with some examples of application (mainly of a probabilistic flavor)

Dynkin (λ-) and π-systems; monotone classes of sets, and of functions with some examples of application (mainly of a probabilistic flavor) Dynkin (λ-) and π-systems; monotone classes of sets, and of functions with some examples of application (mainly of a probabilistic flavor) Matija Vidmar February 7, 2018 1 Dynkin and π-systems Some basic

More information

Copyright c 2007 Jason Underdown Some rights reserved. statement. sentential connectives. negation. conjunction. disjunction

Copyright c 2007 Jason Underdown Some rights reserved. statement. sentential connectives. negation. conjunction. disjunction Copyright & License Copyright c 2007 Jason Underdown Some rights reserved. statement sentential connectives negation conjunction disjunction implication or conditional antecedant & consequent hypothesis

More information

CHAPTER I THE RIESZ REPRESENTATION THEOREM

CHAPTER I THE RIESZ REPRESENTATION THEOREM CHAPTER I THE RIESZ REPRESENTATION THEOREM We begin our study by identifying certain special kinds of linear functionals on certain special vector spaces of functions. We describe these linear functionals

More information

Notes on Measure, Probability and Stochastic Processes. João Lopes Dias

Notes on Measure, Probability and Stochastic Processes. João Lopes Dias Notes on Measure, Probability and Stochastic Processes João Lopes Dias Departamento de Matemática, ISEG, Universidade de Lisboa, Rua do Quelhas 6, 1200-781 Lisboa, Portugal E-mail address: jldias@iseg.ulisboa.pt

More information

U e = E (U\E) e E e + U\E e. (1.6)

U e = E (U\E) e E e + U\E e. (1.6) 12 1 Lebesgue Measure 1.2 Lebesgue Measure In Section 1.1 we defined the exterior Lebesgue measure of every subset of R d. Unfortunately, a major disadvantage of exterior measure is that it does not satisfy

More information

1.1. MEASURES AND INTEGRALS

1.1. MEASURES AND INTEGRALS CHAPTER 1: MEASURE THEORY In this chapter we define the notion of measure µ on a space, construct integrals on this space, and establish their basic properties under limits. The measure µ(e) will be defined

More information

Math212a1411 Lebesgue measure.

Math212a1411 Lebesgue measure. Math212a1411 Lebesgue measure. October 14, 2014 Reminder No class this Thursday Today s lecture will be devoted to Lebesgue measure, a creation of Henri Lebesgue, in his thesis, one of the most famous

More information

Measure Theory on Topological Spaces. Course: Prof. Tony Dorlas 2010 Typset: Cathal Ormond

Measure Theory on Topological Spaces. Course: Prof. Tony Dorlas 2010 Typset: Cathal Ormond Measure Theory on Topological Spaces Course: Prof. Tony Dorlas 2010 Typset: Cathal Ormond May 22, 2011 Contents 1 Introduction 2 1.1 The Riemann Integral........................................ 2 1.2 Measurable..............................................

More information

18.175: Lecture 2 Extension theorems, random variables, distributions

18.175: Lecture 2 Extension theorems, random variables, distributions 18.175: Lecture 2 Extension theorems, random variables, distributions Scott Sheffield MIT Outline Extension theorems Characterizing measures on R d Random variables Outline Extension theorems Characterizing

More information

Section 2: Classes of Sets

Section 2: Classes of Sets Section 2: Classes of Sets Notation: If A, B are subsets of X, then A \ B denotes the set difference, A \ B = {x A : x B}. A B denotes the symmetric difference. A B = (A \ B) (B \ A) = (A B) \ (A B). Remarks

More information

Real Analysis Chapter 1 Solutions Jonathan Conder

Real Analysis Chapter 1 Solutions Jonathan Conder 3. (a) Let M be an infinite σ-algebra of subsets of some set X. There exists a countably infinite subcollection C M, and we may choose C to be closed under taking complements (adding in missing complements

More information

Daniel Akech Thiong Math 501: Real Analysis Homework Problems with Solutions Fall Problem. 1 Give an example of a mapping f : X Y such that

Daniel Akech Thiong Math 501: Real Analysis Homework Problems with Solutions Fall Problem. 1 Give an example of a mapping f : X Y such that Daniel Akech Thiong Math 501: Real Analysis Homework Problems with Solutions Fall 2014 Problem. 1 Give an example of a mapping f : X Y such that 1. f(a B) = f(a) f(b) for some A, B X. 2. f(f 1 (A)) A for

More information

MAT 571 REAL ANALYSIS II LECTURE NOTES. Contents. 2. Product measures Iterated integrals Complete products Differentiation 17

MAT 571 REAL ANALYSIS II LECTURE NOTES. Contents. 2. Product measures Iterated integrals Complete products Differentiation 17 MAT 57 REAL ANALSIS II LECTURE NOTES PROFESSOR: JOHN QUIGG SEMESTER: SPRING 205 Contents. Convergence in measure 2. Product measures 3 3. Iterated integrals 4 4. Complete products 9 5. Signed measures

More information

36-752: Lecture 1. We will use measures to say how large sets are. First, we have to decide which sets we will measure.

36-752: Lecture 1. We will use measures to say how large sets are. First, we have to decide which sets we will measure. 0 0 0 -: Lecture How is this course different from your earlier probability courses? There are some problems that simply can t be handled with finite-dimensional sample spaces and random variables that

More information

Lecture 1 Real and Complex Numbers

Lecture 1 Real and Complex Numbers Lecture 1 Real and Complex Numbers Exercise 1.1. Show that a bounded monotonic increasing sequence of real numbers converges (to its least upper bound). Solution. (This was indicated in class) Let (a n

More information

Real Analysis Comprehensive Exam Fall A(k, ε) is of Lebesgue measure zero.

Real Analysis Comprehensive Exam Fall A(k, ε) is of Lebesgue measure zero. Real Analysis Comprehensive Exam Fall 2002 by XYC Good luck! [1] For ε>0andk>0, denote by A(k, ε) thesetofx Rsuch that x p q 1 for any integers p, q with q 0. k q 2+ε Show that R \ k=1 A(k, ε) is of Lebesgue

More information

The Lebesgue Integral

The Lebesgue Integral The Lebesgue Integral Brent Nelson In these notes we give an introduction to the Lebesgue integral, assuming only a knowledge of metric spaces and the iemann integral. For more details see [1, Chapters

More information

Compendium and Solutions to exercises TMA4225 Foundation of analysis

Compendium and Solutions to exercises TMA4225 Foundation of analysis Compendium and Solutions to exercises TMA4225 Foundation of analysis Ruben Spaans December 6, 2010 1 Introduction This compendium contains a lexicon over definitions and exercises with solutions. Throughout

More information

Chapter 1: Probability Theory Lecture 1: Measure space and measurable function

Chapter 1: Probability Theory Lecture 1: Measure space and measurable function Chapter 1: Probability Theory Lecture 1: Measure space and measurable function Random experiment: uncertainty in outcomes Ω: sample space: a set containing all possible outcomes Definition 1.1 A collection

More information

MATH41011/MATH61011: FOURIER SERIES AND LEBESGUE INTEGRATION. Extra Reading Material for Level 4 and Level 6

MATH41011/MATH61011: FOURIER SERIES AND LEBESGUE INTEGRATION. Extra Reading Material for Level 4 and Level 6 MATH41011/MATH61011: FOURIER SERIES AND LEBESGUE INTEGRATION Extra Reading Material for Level 4 and Level 6 Part A: Construction of Lebesgue Measure The first part the extra material consists of the construction

More information

HILBERT SPACES AND THE RADON-NIKODYM THEOREM. where the bar in the first equation denotes complex conjugation. In either case, for any x V define

HILBERT SPACES AND THE RADON-NIKODYM THEOREM. where the bar in the first equation denotes complex conjugation. In either case, for any x V define HILBERT SPACES AND THE RADON-NIKODYM THEOREM STEVEN P. LALLEY 1. DEFINITIONS Definition 1. A real inner product space is a real vector space V together with a symmetric, bilinear, positive-definite mapping,

More information

The Kolmogorov extension theorem

The Kolmogorov extension theorem The Kolmogorov extension theorem Jordan Bell jordan.bell@gmail.com Department of Mathematics, University of Toronto June 21, 2014 1 σ-algebras and semirings If X is a nonempty set, an algebra of sets on

More information

Real Analysis II, Winter 2018

Real Analysis II, Winter 2018 Real Analysis II, Winter 2018 From the Finnish original Moderni reaalianalyysi 1 by Ilkka Holopainen adapted by Tuomas Hytönen January 18, 2018 1 Version dated September 14, 2011 Contents 1 General theory

More information

ABSTRACT INTEGRATION CHAPTER ONE

ABSTRACT INTEGRATION CHAPTER ONE CHAPTER ONE ABSTRACT INTEGRATION Version 1.1 No rights reserved. Any part of this work can be reproduced or transmitted in any form or by any means. Suggestions and errors are invited and can be mailed

More information

(1) Consider the space S consisting of all continuous real-valued functions on the closed interval [0, 1]. For f, g S, define

(1) Consider the space S consisting of all continuous real-valued functions on the closed interval [0, 1]. For f, g S, define Homework, Real Analysis I, Fall, 2010. (1) Consider the space S consisting of all continuous real-valued functions on the closed interval [0, 1]. For f, g S, define ρ(f, g) = 1 0 f(x) g(x) dx. Show that

More information

Lecture 3: Probability Measures - 2

Lecture 3: Probability Measures - 2 Lecture 3: Probability Measures - 2 1. Continuation of measures 1.1 Problem of continuation of a probability measure 1.2 Outer measure 1.3 Lebesgue outer measure 1.4 Lebesgue continuation of an elementary

More information

JORDAN CONTENT. J(P, A) = {m(i k ); I k an interval of P contained in int(a)} J(P, A) = {m(i k ); I k an interval of P intersecting cl(a)}.

JORDAN CONTENT. J(P, A) = {m(i k ); I k an interval of P contained in int(a)} J(P, A) = {m(i k ); I k an interval of P intersecting cl(a)}. JORDAN CONTENT Definition. Let A R n be a bounded set. Given a rectangle (cartesian product of compact intervals) R R n containing A, denote by P the set of finite partitions of R by sub-rectangles ( intervals

More information

Probability and Measure

Probability and Measure Part II Year 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2018 84 Paper 4, Section II 26J Let (X, A) be a measurable space. Let T : X X be a measurable map, and µ a probability

More information

MAT 570 REAL ANALYSIS LECTURE NOTES. Contents. 1. Sets Functions Countability Axiom of choice Equivalence relations 9

MAT 570 REAL ANALYSIS LECTURE NOTES. Contents. 1. Sets Functions Countability Axiom of choice Equivalence relations 9 MAT 570 REAL ANALYSIS LECTURE NOTES PROFESSOR: JOHN QUIGG SEMESTER: FALL 204 Contents. Sets 2 2. Functions 5 3. Countability 7 4. Axiom of choice 8 5. Equivalence relations 9 6. Real numbers 9 7. Extended

More information

Measure Theory & Integration

Measure Theory & Integration Measure Theory & Integration Lecture Notes, Math 320/520 Fall, 2004 D.H. Sattinger Department of Mathematics Yale University Contents 1 Preliminaries 1 2 Measures 3 2.1 Area and Measure........................

More information

FUNDAMENTALS OF REAL ANALYSIS by. II.1. Prelude. Recall that the Riemann integral of a real-valued function f on an interval [a, b] is defined as

FUNDAMENTALS OF REAL ANALYSIS by. II.1. Prelude. Recall that the Riemann integral of a real-valued function f on an interval [a, b] is defined as FUNDAMENTALS OF REAL ANALYSIS by Doğan Çömez II. MEASURES AND MEASURE SPACES II.1. Prelude Recall that the Riemann integral of a real-valued function f on an interval [a, b] is defined as b n f(xdx :=

More information

Recall that if X is a compact metric space, C(X), the space of continuous (real-valued) functions on X, is a Banach space with the norm

Recall that if X is a compact metric space, C(X), the space of continuous (real-valued) functions on X, is a Banach space with the norm Chapter 13 Radon Measures Recall that if X is a compact metric space, C(X), the space of continuous (real-valued) functions on X, is a Banach space with the norm (13.1) f = sup x X f(x). We want to identify

More information

Lebesgue Measure. Chapter Lebesgue Outer Measure

Lebesgue Measure. Chapter Lebesgue Outer Measure Chapter 2 Lebesgue Measure This chapter develops the basic notions of measure theory. They are what is needed to introduce the concepts of measure-preserving transformations, recurrence and ergodicity

More information

Real Analysis Problems

Real Analysis Problems Real Analysis Problems Cristian E. Gutiérrez September 14, 29 1 1 CONTINUITY 1 Continuity Problem 1.1 Let r n be the sequence of rational numbers and Prove that f(x) = 1. f is continuous on the irrationals.

More information

STAT 7032 Probability Spring Wlodek Bryc

STAT 7032 Probability Spring Wlodek Bryc STAT 7032 Probability Spring 2018 Wlodek Bryc Created: Friday, Jan 2, 2014 Revised for Spring 2018 Printed: January 9, 2018 File: Grad-Prob-2018.TEX Department of Mathematical Sciences, University of Cincinnati,

More information

Point sets and certain classes of sets

Point sets and certain classes of sets 1 Point sets and certain classes of sets 1.1 Points, sets and classes We shall consider sets consisting of elements or points. The nature of the points will be left unspecified examples are points in a

More information

Defining the Integral

Defining the Integral Defining the Integral In these notes we provide a careful definition of the Lebesgue integral and we prove each of the three main convergence theorems. For the duration of these notes, let (, M, µ) be

More information