Week 2. Review of Probability, Random Variables and Univariate Distributions

Transcription

1 Week 2 Review of Probability, Random Variables and Univariate Distributions

2 Probability Probability

3 Probability Motivation What use is Probability Theory? Probability models Basis for statistical inference

4 Probability Outcomes and Events Outcomes and Events Elementary Events the distinguishable outcomes of an experiment Sample Space set of all elementary events Event subset of the sample space Event Space all events associated with an experiment We work on a Probability space which consists of a sample space Ω and a σ-algebra of events. A σ-algebra is a collection of sets which 1. contains the empty-set, 2. contains all complements 3. and is stable under countable unions Example: Consider the roll of a die. What is an appropriate sample space and algebra of events?

5 Probability Outcomes and Events Exercise 2.1 Prove that a set of size M (sample space) has 2 M subsets (event space)

6 Probability Probability Function Probability Function Probability Function a set function with domain A (a σ-algebra of events) and counterdomain the interval [0,1] satisfying 1. P [A] 0 for every A A 2. P [Ω] = 1 3. If A 1, A 2,... is a sequence of mutually exclusive events in A (so that A i A), then i=1 [ ] P A i = i=1 P [A i ] i=1 Example: For the roll of the die it is natural to take Ω = {1, 2, 3, 4, 5, 6} and A to be the set of all subsets of Ω. Then P (A) = Card(A)/Card(Ω).

7 Probability Probability Function Basic Rules of Probability If A 1, A 2,..., A n are mutually exclusive elements in A, then P [A 1 A 2... A n ] = n P [A i ] i=1 If A A then For every two events A, B A P [Ā] = 1 P [A] P [A B] = P [A] + P [B] P [AB] If A, B A and A B, then Here AB is shorthand for A B. P [A] P [B]

8 Probability Conditional Probability and Independence Conditional Probability Conditional Probability the probability of event A given B P [A B] = P [AB] P [B] if P [B] > 0 (undefined for P[B] = 0)

9 Probability Conditional Probability and Independence Rules of Conditional Probability Theorem of Total Probability If B 1,..., B n partition Ω and P [B j ] > 0, j = 1,..., n, then n P [A] = P [A B j ]P [B j ] j=1 Bayes Formula If B 1,..., B n partition Ω and P [B j ] > 0, j = 1,..., n, then for every A A for which P [A] > 0, P [B k A] = P [A B k ]P [B k ] n j=1 P [A B j]p [B j ] Multiplication Rule If A 1,..., A n A and P [A 1,... A n 1 ] > 0, then P [A 1,... A n ] = P [A 1 ]P [A 2 A 1 ]... P [A n A 1... A n 1 ]

10 Probability Conditional Probability and Independence Exercise 2.2 There are 5 urns, numbered 1 to 5. Each urn contains 10 balls. Urn i has i defective balls, i = 1,..., 5. Consider the following experiment: First an urn is selected at random and then a ball is selected at random from the selected urn. The experimenter does not know which urn was selected. 1. What is the probability that a defective ball will be selected? 2. If we have already selected the ball and noted that it is defective, what is the probability that it came from urn 5? Generalise to urn k, k = 1,..., 5

11 Probability Conditional Probability and Independence Independent Events Independent Events events A and B are independent iff P [AB] = P [A]P [B]. It follows that 1. P [A B] = P [A] if P [B] > 0 2. P [B A] = P [B] if P [A] > 0

12 Probability Conditional Probability and Independence Exercise 2.3 Consider the experiment of tossing two dice. Let A = {total is odd}, B = {6 on the first die}, C = {total is seven}. 1. Are A and B independent? 2. Are A and C independent? 3. Are B and C independent?

13 Probability Conditional Probability and Independence Independence of Several Events Independent Events events A 1,..., A n are independent iff 1. P [A i A j ] = P [A i ]P [A j ], i j 2. P [A i A j A k ] = P [A i ]P [A j ]P [A k ], i j, j k, i k... n. P [ n i=1 A i] = n i=1 P [A i]

14 Probability Conditional Probability and Independence Exercise 2.4 Show that pairwise independence does not imply independence using the following events in the random experiment of tossing two dice: 1. A 1 = {odd face on first die} 2. A 2 = {odd face on second die} 3. A 3 = {odd total}

15 Random Variables Random Variables

16 Random Variables Random Variables and Cumulative Distribution Function Random Variables and CDF Random Variable For a given probability space (Ω, A, P [.]), a function with domain Ω and counterdomain the real line. Distribution Function The function F X : R [0, 1] such that for every x R. F X (x) = P [X x] = P [{ω : X(ω) x}]

17 Random Variables Random Variables and Cumulative Distribution Function Properties of CDFs 1. lim F X(x) = 0 and lim F X(X) = 1 x x 2. F X (a) F X (b) for a < b 3. F X (.) is continuous from the right lim F X (x + h) = F X (x) h 0

18 Random Variables Random Variables and Cumulative Distribution Function Exercise 2.5 Consider the experiment of tossing two dice. Let X = {total of upturned faces} and Y = {absolute difference of upturned faces}. Sketch F Y.

19 Random Variables Discrete Random Variables Mass Functions A random variable is discrete if the set of values it can take is countable. For random variable X with distinct values x 1, x 2,..., x n,..., the mass function p X : R [0, 1] such that { P [X = x j ] if x = x j, j = 1, 2,..., n,... p X (x) = 0 if x x j

20 Random Variables Discrete Random Variables Properties of Mass Functions 1. p(x j ) 0, j = 1, 2, p(x) = 0 for x x j, j = 1, 2, j p(x j) = 1 where summation is over x 1, x 2,..., x n,...

21 Random Variables Discrete Random Variables Exercise 2.6 Consider the experiment of tossing two dice. Let X = {total of upturned faces} and Y = {absolute difference of upturned faces}. Give the probability function p X and sketch it. Give p Y.

22 Random Variables Expectations and Moments Expectation For discrete X with mass points x 1, x 2,..., x j,... E[X] = j x j p X (x j )

23 Random Variables Expectations and Moments Let Y = g(x). Then Y is a discrete random variable. Y takes values y i = g(x i ). If g is strictly monotonic then the y i are distinct and p Y (y i ) = p X (x i ). More generally p Y (y i ) = j:g(x j )=y i p(x j ). Provided the sums below are absolutely convergent we have E[g(X)] = E[Y ] = y k p Y (y k ) = y k p(x j ) k k j:g(x j )=y k = g(x j )p X (x j ) k j:g(x j )=y k = j g(x j )p X (x j )

24 Random Variables Expectations and Moments Variance Let X be a random variable and let µ X = E[X]. Discrete For discrete X with mass points x 1, x 2,..., x j,... V ar[x] = j (x j µ x ) 2 f X (x j ) Variance in terms of expectations V ar[x] = E[(X E(X) 2 )] = E[X 2 ] (E[X]) 2

25 Random Variables Expectations and Moments Exercise 2.7 Consider the experiment of tossing two dice. Let X = {total of upturned faces} and Y = {absolute difference of upturned faces}. Compute E[X] and E[Y].

26 Random Variables Expectations and Moments Properties of Expectations 1. E[c] = c for a constant c 2. E[cg(X)] = ce[g(x)] for a constant c 3. E[c 1 g 1 (X) + c 2 g 2 (X)] = c 1 E[g 1 (X)] + c 2 E[g 2 (X)] 4. E[g 1 (X)] E[g 2 (X)] if g 1 (x) g 2 (x) x

27 Random Variables Expectations and Moments Two Useful Results Chebyshev Inequality For a RV X with finite variance P [ X µ X rσ X ] = P [(X µ X ) 2 r 2 σ 2 X] 1 r 2 Jensen Inequality For a RV X with mean E[X] and g(.) a convex function, E[g(X)] g(e[x])

28 Random Variables Moments and Moment Generating Functions Moments Moments For a RV X, the r th moment of X is given by µ r = E[X r ] if the expectation exists Central Moments For a RV X, the r th central moment of X about a is given by µ r = E[(X a) r ]

29 Random Variables Moments and Moment Generating Functions The First Four Moments about the Mean µ 1 = E[(X µ X )] = 0 variance skewness kurtosis µ 2 = E[(X µ X ) 2 ] µ 3 σx 3 µ 4 σx 4 = E[(X µ X) 3 ] σ 3 X = E[(X µ X) 4 ] σ 4 X

30 Random Variables Moments and Moment Generating Functions Moment Generating Function Let X be a RV with mass function p X (.) Discrete m(t) = E[e tx ] = e tx p X (x) x The MGF has the property that dm(t) dt = µ X t=0 This can be extended to d r m(t) dt r = µ r t=0

31 Random Variables Moments and Moment Generating Functions Exercise 2.8 Find the MGF of the binomial distribution ( ) n P (X = x) = p x (1 p) n x, x = 0, 1, 2,..., n x Use it to show that the mean is np and the variance is np(1 p)

32 Random Variables Moments and Moment Generating Functions Other Distribution Summaries Quantile For a RV X, the q th quantile η q is the smallest number η satisfying F X (η) q Median Interquartile Range η 0.5 η 0.75 η 0.25 Mode the point at which p X (.) obtains its maximum

33 Special Univariate Distributions Special Univariate Distributions

34 Special Univariate Distributions Discrete Distributions Discrete Uniform Distribution p(x) = { 1 N x = 1, 2, 3,..., N 0 otherwise E[X] = N V ar[x] = N Discrete Uniform(10) f(x) 1/ x

35 Special Univariate Distributions Discrete Distributions Bernoulli Distribution p(x) = { p x (1 p) 1 x x = 0, 1 0 p 1 0 otherwise E[X] = p V ar[x] = p(1 p) = pq Bernoulli(0.4) 0.6 f(x) x

36 Special Univariate Distributions Discrete Distributions Binomial Distribution {( n ) p(x) = x p x (1 p) n x x = 0, 1,..., n 0 p 1 0 otherwise E[X] = np V ar[x] = np(1 p) f(x) Bin(20, 0.2) x

37 Special Univariate Distributions Discrete Distributions Hypergeometric Distribution ( A B ) x)( n x ( p(x; A, B, n) = A+B ) x = 0, 1,..., n n 0 otherwise E[X] = np V ar[x] = npq T n T 1 T = A + B, p = A T f(x) Hypergeometric(10, 5, 5) x

38 Special Univariate Distributions Discrete Distributions Poisson Distribution p(x; λ) = { e λ λ x x! x = 0, 1,... λ > 0 0 otherwise E[X] = V ar[x] = λ Poisson(2) f(x) x

39 Special Univariate Distributions Discrete Distributions Exercise 2.9 Prove that E[X] = V ar[x] = λ using the MGF of the Poisson Distribution

40 Special Univariate Distributions Discrete Distributions Uses of the Poisson Distribution For large n, X Bin(n, p) is approximately P oisson(np) A Poisson Process with rate λ per unit time is such that 1. X, the number of occurences of an event in a given time interval t is P oisson(λt) 2. The number of events in non-overlapping time intervals are independent

41 Special Univariate Distributions Discrete Distributions Exercise 2.10 The number X of insect larvae found on a cm 2 on a petri plate is assumed to follow a Poisson distribution with λ = 3. Find P (X 3) P (X > 1) P (2 X 4) F (4.2) Find an example (plot the distribution on a petri plate) where the assumption of a Poisson distribution would not be reasonable.

42 Special Univariate Distributions Discrete Distributions Geometric and Negative Binomial Distribution p(x; p, r) = {( x+r 1 x where 0 p 1, q = 1 p ) p r q x = ( r x 0 otherwise E[X] = rq p ) p r ( q) x x = 0, 1,... V ar[x] = rq p 2 f(x) NB(1, 0.4) = Geometric(0.4) x

43 Special Univariate Distributions Discrete Distributions Exercise 2.11 Let X NB(r, p). Find its MGF and use it to derive E[X] and V ar[x].