Size: px
Start display at page:

Download ""

Transcription

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15 Deductive Logic Probability Theory

16

17 π X X X X = X X =

18 x f z x Physics f Sensor z x f f z

19 P(X = x) X x X x P(X = x) = 1 /6 P(X = x) P(x) F(x) F(x) = P(X x)

20 X Y Y Z X Z X X Y X Y X, Y = X Y

21 P(a) = 1. a P(a, b) = P(a) P(b a) = P(b) P(a b) P(a, b) = P(a b) = P(a b) A B S

22 P(a, b) = P(a) P(b a) = P(b) P(a b). P(A, B, C) P(B A, C) P(A) P(C A) P(A) P(B A) P(C B) P(A B, C) P(B) P(C A) P(A) P(B) P(C)

23 P(A, B, C) P(B A, C) P(A) P(C A) P(A) P(B A) P(C B) P(A B, C) P(B) P(C A) P(A) P(B) P(C) P(A, B, C) = P(A B, C) P(B, C) = P(A B, C) P(B C) P(C) = P(A B, C) P(C B) P(B) = P(C B, A) P(B A) P(A)

24 S H V V P(H V) = 0.95 P(V) = 10 6 P(H, V) = P(H V)P(V) =

25 b a b P(a i, b j ) = 1 a i P(a i ) = j j P(a i, b j ) x y x y P(x, y) x = 0 x = 1 x = 2 y = y = y =

26 P(a i ) = j P(a i, b j ) P(x, y) x = 0 x = 1 x = 2 y = y = y = P(Y = 0)

27 P(a i ) = j P(a i, b j ) P(x, y) x = 0 x = 1 x = 2 y = y = y = P(Y = 0)

28 P(x, y) x = 0 x = 1 x = 2 y = y = y = = 1.0 P(X = 1) = j P(X = 1, y j) = 0.30 P(Y = 0) = i P(x i, Y = 0) = 0.36

29 P(y, x) = P(y x) P(x) = P(y x) = P(y,x) P(x). P(x, y) x = 0 x = 1 x = 2 y = y = y = P(Y = 0 X = 1)

30 P(y, x) = P(y x) P(x) = P(y x) = P(y,x) P(x). P(x, y) x = 0 x = 1 x = 2 y = y = y = P(Y = 0 X = 1)

31 P(x, y) x = 0 x = 1 x = 2 y = y = y = Y = y X = 1 P(y X = 1) = P(X = 1, y) P(X = 1) P(y X = 1) y = /0.30 = 0.1 y = /0.30 = 0.8 y = /0.30 =

32 P(a b) = P(b a) P(a) P(b) P(a b) a P(a) a P(b a) a a L(a) = P(b a) P(b) P(b) = i P(a i, b) = i P(b a i)p(a i ).

33

34 f 1,2,3 P(f i w) = P(w f i)p(f i ) = P(w f i)p(f i ) P(w) j P(w f j)p(f j ) i P(w f i ) P(f i ) P(w f i )P(f i ) P(f 1 w) = 2.0 P(f 2 w) = 2.1 P(f 3 w) =

35 A B P(a, b) = P(a) P(b) P(a, b) = P(a b)p(b) P(a, b) = P(a)P(b) P(a)P(b) = P(a b)p(b) P(a b) = P(a) A B

36 H 1 T 1 H 2 T 2 [ 1 /4 ] [ ] 1/4 1 /2 [ = 1 /2 1/2 ] 1/4 1/4 1/2

37 P(x, y) x = 0 x = 1 x = 2 y = 0 y = 1 y = 2 P(x, y) x = 0 x = 1 x = 2 y = 0 y = 1 y = 2 y y y x x x

38 Y D N P(y, d, n) P(y d) = = P(y, d) y P(y, d) n P(y, d, n) P(y, d, n). y n

39 N B F B F N

40 N N N P(B, F, N) = P(B) P(F) P(N F, B). B F p(b, F) = p(b) p(f) B F N

41 P(B, F, N) = P(B) P(F) P(N F, B). P(B N) = b f f P(B) P(F = f) P(N F = f, B) P(B = b) P(F = f) P(N F = f, B = b). P(B) = 0.001, P(F) = 0.1, P(N f, b) = b = b = ( ) f = , f = P(B N) = ,

42 P(A, B, C, D, E, F) = P(A) P(B) P(C A, B) P(D B) P(E D) P(F D). A B C D E F

43 P(B) P(B) P(A, B) = P(A)P(B A) = P(B)P(A B) A B A B

44 P A B B A A B P P A B A P B A P B

45 A B P P A B A P B A P B

46 A B P P

47 B F N N p(b, F N) = p(b N) p(f N) B F N N B F

48 A E C E B F A B A B C D C D E F E F

49 X Y D D D X Y X Y X Y D D X Y X Y

50 E[X] = m(x) = X = µ = i x i P(x i ) X E[X] = = 1.7 E E[aX + by] = ae[x] + be[y]

51 f X A E[f(X) A] = i f(x i )P(x i A) A n E[x n ] = i x n i P(x i ) n E[(x µ) n ] = i (x i µ) n P(x i )

52 var(x) = E[(x µ) 2 ] = i (x i µ) 2 P(x i ) (X) = σ = (x) X = 1 X = 1 (X) = ( 1 0) 2 1 /2 + ( 1 0) 2 1 /2 = 1 (X) = (1 0.80) ( ) = 0.60

53

54 A B P(a, b) = P(a)P(b)

Lecture 10: Bayes' Theorem, Expected Value and Variance Lecturer: Lale Özkahya

Lecture 10: Bayes' Theorem, Expected Value and Variance Lecturer: Lale Özkahya BBM 205 Discrete Mathematics Hacettepe University http://web.cs.hacettepe.edu.tr/ bbm205 Lecture 10: Bayes' Theorem, Expected Value and Variance Lecturer: Lale Özkahya Resources: Kenneth Rosen, Discrete

More information

Exam 1 - Math Solutions

Exam 1 - Math Solutions Exam 1 - Math 3200 - Solutions Spring 2013 1. Without actually expanding, find the coefficient of x y 2 z 3 in the expansion of (2x y z) 6. (A) 120 (B) 60 (C) 30 (D) 20 (E) 10 (F) 10 (G) 20 (H) 30 (I)

More information

STAT 430/510 Probability Lecture 7: Random Variable and Expectation

STAT 430/510 Probability Lecture 7: Random Variable and Expectation STAT 430/510 Probability Lecture 7: Random Variable and Expectation Pengyuan (Penelope) Wang June 2, 2011 Review Properties of Probability Conditional Probability The Law of Total Probability Bayes Formula

More information

Data Modeling & Analysis Techniques. Probability & Statistics. Manfred Huber

Data Modeling & Analysis Techniques. Probability & Statistics. Manfred Huber Data Modeling & Analysis Techniques Probability & Statistics Manfred Huber 2017 1 Probability and Statistics Probability and statistics are often used interchangeably but are different, related fields

More information

Introduction to Statistical Inference Self-study

Introduction to Statistical Inference Self-study Introduction to Statistical Inference Self-study Contents Definition, sample space The fundamental object in probability is a nonempty sample space Ω. An event is a subset A Ω. Definition, σ-algebra A

More information

Math438 Actuarial Probability

Math438 Actuarial Probability Math438 Actuarial Probability Jinguo Lian Department of Math and Stats Jan. 22, 2016 Continuous Random Variables-Part I: Definition A random variable X is continuous if its set of possible values is an

More information

Mutually Exclusive Events

Mutually Exclusive Events 172 CHAPTER 3 PROBABILITY TOPICS c. QS, 7D, 6D, KS Mutually Exclusive Events A and B are mutually exclusive events if they cannot occur at the same time. This means that A and B do not share any outcomes

More information

Probability. Paul Schrimpf. January 23, UBC Economics 326. Probability. Paul Schrimpf. Definitions. Properties. Random variables.

Probability. Paul Schrimpf. January 23, UBC Economics 326. Probability. Paul Schrimpf. Definitions. Properties. Random variables. Probability UBC Economics 326 January 23, 2018 1 2 3 Wooldridge (2013) appendix B Stock and Watson (2009) chapter 2 Linton (2017) chapters 1-5 Abbring (2001) sections 2.1-2.3 Diez, Barr, and Cetinkaya-Rundel

More information

Random Variables. Saravanan Vijayakumaran Department of Electrical Engineering Indian Institute of Technology Bombay

Random Variables. Saravanan Vijayakumaran Department of Electrical Engineering Indian Institute of Technology Bombay 1 / 13 Random Variables Saravanan Vijayakumaran sarva@ee.iitb.ac.in Department of Electrical Engineering Indian Institute of Technology Bombay August 8, 2013 2 / 13 Random Variable Definition A real-valued

More information

Continuous Random Variables

Continuous Random Variables 1 / 24 Continuous Random Variables Saravanan Vijayakumaran sarva@ee.iitb.ac.in Department of Electrical Engineering Indian Institute of Technology Bombay February 27, 2013 2 / 24 Continuous Random Variables

More information

Introduction to Probability 2017/18 Supplementary Problems

Introduction to Probability 2017/18 Supplementary Problems Introduction to Probability 2017/18 Supplementary Problems Problem 1: Let A and B denote two events with P(A B) 0. Show that P(A) 0 and P(B) 0. A A B implies P(A) P(A B) 0, hence P(A) 0. Similarly B A

More information

Bayesian statistics, simulation and software

Bayesian statistics, simulation and software Module 1: Course intro and probability brush-up Department of Mathematical Sciences Aalborg University 1/22 Bayesian Statistics, Simulations and Software Course outline Course consists of 12 half-days

More information

Dept. of Linguistics, Indiana University Fall 2015

Dept. of Linguistics, Indiana University Fall 2015 L645 Dept. of Linguistics, Indiana University Fall 2015 1 / 34 To start out the course, we need to know something about statistics and This is only an introduction; for a fuller understanding, you would

More information

STAT 302: Assignment 1

STAT 302: Assignment 1 STAT 302: Assignment 1 Due date: Feb 4, 2011. Hand to Mailbox besides LSK333 1. An imaginary small university has 3 programs A, B and C. If an applicant is a girl, her probability of being admitted to

More information

STAT 430/510: Lecture 10

STAT 430/510: Lecture 10 STAT 430/510: Lecture 10 James Piette June 9, 2010 Updates HW2 is due today! Pick up your HW1 s up in stat dept. There is a box located right when you enter that is labeled "Stat 430 HW1". It ll be out

More information

STA 2023 EXAM-2 Practice Problems From Chapters 4, 5, & Partly 6. With SOLUTIONS

STA 2023 EXAM-2 Practice Problems From Chapters 4, 5, & Partly 6. With SOLUTIONS STA 2023 EXAM-2 Practice Problems From Chapters 4, 5, & Partly 6 With SOLUTIONS Mudunuru Venkateswara Rao, Ph.D. STA 2023 Fall 2016 Venkat Mu ALL THE CONTENT IN THESE SOLUTIONS PRESENTED IN BLUE AND BLACK

More information

Introduction to Machine Learning

Introduction to Machine Learning Introduction to Machine Learning Introduction to Probabilistic Methods Varun Chandola Computer Science & Engineering State University of New York at Buffalo Buffalo, NY, USA chandola@buffalo.edu Chandola@UB

More information

MA : Introductory Probability

MA : Introductory Probability MA 320-001: Introductory Probability David Murrugarra Department of Mathematics, University of Kentucky http://www.math.uky.edu/~dmu228/ma320/ Spring 2017 David Murrugarra (University of Kentucky) MA 320:

More information

STA 2023 EXAM-2 Practice Problems. Ven Mudunuru. From Chapters 4, 5, & Partly 6. With SOLUTIONS

STA 2023 EXAM-2 Practice Problems. Ven Mudunuru. From Chapters 4, 5, & Partly 6. With SOLUTIONS STA 2023 EXAM-2 Practice Problems From Chapters 4, 5, & Partly 6 With SOLUTIONS Mudunuru, Venkateswara Rao STA 2023 Spring 2016 1 1. A committee of 5 persons is to be formed from 6 men and 4 women. What

More information

Massachusetts Institute of Technology

Massachusetts Institute of Technology 6.04/6.4: Probabilistic Systems Analysis Fall 00 Quiz Solutions: October, 00 Problem.. 0 points Let R i be the amount of time Stephen spends at the ith red light. R i is a Bernoulli random variable with

More information

Math 511 Exam #1. Show All Work No Calculators

Math 511 Exam #1. Show All Work No Calculators Math 511 Exam #1 Show All Work No Calculators 1. Suppose that A and B are events in a sample space S and that P(A) = 0.4 and P(B) = 0.6 and P(A B) = 0.3. Suppose too that B, C, and D are mutually independent

More information

P(T = 7) = P(T = 7 A = n)p(a = n) = P(B = 7 - n)p(a = n) = P(B =4)P(A = 3) = = 0.06

P(T = 7) = P(T = 7 A = n)p(a = n) = P(B = 7 - n)p(a = n) = P(B =4)P(A = 3) = = 0.06 3.1 Total time T = A + B, which ranges from (3 + 4 = 7) to (5 + 6 = 11). Divide the sample space into A = 3, A = 4, and A = 5 (m.e. & c.e. events) Similarly P(T = 7) = P(T = 7 A = n)p(a = n) n345,, = P(B

More information

Homework 4 Solution, due July 23

Homework 4 Solution, due July 23 Homework 4 Solution, due July 23 Random Variables Problem 1. Let X be the random number on a die: from 1 to. (i) What is the distribution of X? (ii) Calculate EX. (iii) Calculate EX 2. (iv) Calculate Var

More information

Example: Suppose we toss a quarter and observe whether it falls heads or tails, recording the result as 1 for heads and 0 for tails.

Example: Suppose we toss a quarter and observe whether it falls heads or tails, recording the result as 1 for heads and 0 for tails. Example: Suppose we toss a quarter and observe whether it falls heads or tails, recording the result as 1 for heads and 0 for tails. (In Mathematical language, the result of our toss is a random variable,

More information

Probability. Paul Schrimpf. January 23, Definitions 2. 2 Properties 3

Probability. Paul Schrimpf. January 23, Definitions 2. 2 Properties 3 Probability Paul Schrimpf January 23, 2018 Contents 1 Definitions 2 2 Properties 3 3 Random variables 4 3.1 Discrete........................................... 4 3.2 Continuous.........................................

More information

Cheng Soon Ong & Christian Walder. Canberra February June 2018

Cheng Soon Ong & Christian Walder. Canberra February June 2018 Cheng Soon Ong & Christian Walder Research Group and College of Engineering and Computer Science Canberra February June 2018 (Many figures from C. M. Bishop, "Pattern Recognition and ") 1of 143 Part IV

More information

3 Multiple Discrete Random Variables

3 Multiple Discrete Random Variables 3 Multiple Discrete Random Variables 3.1 Joint densities Suppose we have a probability space (Ω, F,P) and now we have two discrete random variables X and Y on it. They have probability mass functions f

More information

Quantitative Methods for Decision Making

Quantitative Methods for Decision Making January 14, 2012 Lecture 3 Probability Theory Definition Mutually exclusive events: Two events A and B are mutually exclusive if A B = φ Definition Special Addition Rule: Let A and B be two mutually exclusive

More information

A Probability Primer. A random walk down a probabilistic path leading to some stochastic thoughts on chance events and uncertain outcomes.

A Probability Primer. A random walk down a probabilistic path leading to some stochastic thoughts on chance events and uncertain outcomes. A Probability Primer A random walk down a probabilistic path leading to some stochastic thoughts on chance events and uncertain outcomes. Are you holding all the cards?? Random Events A random event, E,

More information

(c) Find the product moment correlation coefficient between s and t.

(c) Find the product moment correlation coefficient between s and t. 1. A clothes shop manager records the weekly sales figures, s, and the average weekly temperature, t C, for 6 weeks during the summer. The sales figures were coded so that s w = 1000 The data are summarised

More information

Chapter 4. Continuous Random Variables 4.1 PDF

Chapter 4. Continuous Random Variables 4.1 PDF Chapter 4 Continuous Random Variables In this chapter we study continuous random variables. The linkage between continuous and discrete random variables is the cumulative distribution (CDF) which we will

More information

Probability Review. Chao Lan

Probability Review. Chao Lan Probability Review Chao Lan Let s start with a single random variable Random Experiment A random experiment has three elements 1. sample space Ω: set of all possible outcomes e.g.,ω={1,2,3,4,5,6} 2. event

More information

Cogs 14B: Introduction to Statistical Analysis

Cogs 14B: Introduction to Statistical Analysis Cogs 14B: Introduction to Statistical Analysis Statistical Tools: Description vs. Prediction/Inference Description Averages Variability Correlation Prediction (Inference) Regression Confidence intervals/

More information

Chapter 18 Section 8.5 Fault Trees Analysis (FTA) Don t get caught out on a limb of your fault tree.

Chapter 18 Section 8.5 Fault Trees Analysis (FTA) Don t get caught out on a limb of your fault tree. Chapter 18 Section 8.5 Fault Trees Analysis (FTA) Don t get caught out on a limb of your fault tree. C. Ebeling, Intro to Reliability & Maintainability Engineering, 2 nd ed. Waveland Press, Inc. Copyright

More information

Stochastic Simulation Introduction Bo Friis Nielsen

Stochastic Simulation Introduction Bo Friis Nielsen Stochastic Simulation Introduction Bo Friis Nielsen Applied Mathematics and Computer Science Technical University of Denmark 2800 Kgs. Lyngby Denmark Email: bfn@imm.dtu.dk Practicalities Notes will handed

More information

ISyE 6739 Test 1 Solutions Summer 2015

ISyE 6739 Test 1 Solutions Summer 2015 1 NAME ISyE 6739 Test 1 Solutions Summer 2015 This test is 100 minutes long. You are allowed one cheat sheet. 1. (50 points) Short-Answer Questions (a) What is any subset of the sample space called? Solution:

More information

CS 109 Review. CS 109 Review. Julia Daniel, 12/3/2018. Julia Daniel

CS 109 Review. CS 109 Review. Julia Daniel, 12/3/2018. Julia Daniel CS 109 Review CS 109 Review Julia Daniel, 12/3/2018 Julia Daniel Dec. 3, 2018 Where we re at Last week: ML wrap-up, theoretical background for modern ML This week: course overview, open questions after

More information

CS4705. Probability Review and Naïve Bayes. Slides from Dragomir Radev

CS4705. Probability Review and Naïve Bayes. Slides from Dragomir Radev CS4705 Probability Review and Naïve Bayes Slides from Dragomir Radev Classification using a Generative Approach Previously on NLP discriminative models P C D here is a line with all the social media posts

More information

Discrete time Markov chains. Discrete Time Markov Chains, Definition and classification. Probability axioms and first results

Discrete time Markov chains. Discrete Time Markov Chains, Definition and classification. Probability axioms and first results Discrete time Markov chains Discrete Time Markov Chains, Definition and classification 1 1 Applied Mathematics and Computer Science 02407 Stochastic Processes 1, September 5 2017 Today: Short recap of

More information

SDS 321: Introduction to Probability and Statistics

SDS 321: Introduction to Probability and Statistics SDS 321: Introduction to Probability and Statistics Lecture 13: Expectation and Variance and joint distributions Purnamrita Sarkar Department of Statistics and Data Science The University of Texas at Austin

More information

Probability Theory Review Reading Assignments

Probability Theory Review Reading Assignments Probability Theory Review Reading Assignments R. Duda, P. Hart, and D. Stork, Pattern Classification, John-Wiley, 2nd edition, 2001 (appendix A.4, hard-copy). "Everything I need to know about Probability"

More information

Copyright c 2006 Jason Underdown Some rights reserved. choose notation. n distinct items divided into r distinct groups.

Copyright c 2006 Jason Underdown Some rights reserved. choose notation. n distinct items divided into r distinct groups. Copyright & License Copyright c 2006 Jason Underdown Some rights reserved. choose notation binomial theorem n distinct items divided into r distinct groups Axioms Proposition axioms of probability probability

More information

CME 106: Review Probability theory

CME 106: Review Probability theory : Probability theory Sven Schmit April 3, 2015 1 Overview In the first half of the course, we covered topics from probability theory. The difference between statistics and probability theory is the following:

More information

Part (A): Review of Probability [Statistics I revision]

Part (A): Review of Probability [Statistics I revision] Part (A): Review of Probability [Statistics I revision] 1 Definition of Probability 1.1 Experiment An experiment is any procedure whose outcome is uncertain ffl toss a coin ffl throw a die ffl buy a lottery

More information

Notation: X = random variable; x = particular value; P(X = x) denotes probability that X equals the value x.

Notation: X = random variable; x = particular value; P(X = x) denotes probability that X equals the value x. Ch. 16 Random Variables Def n: A random variable is a numerical measurement of the outcome of a random phenomenon. A discrete random variable is a random variable that assumes separate values. # of people

More information

Probability Review I

Probability Review I Probability Review I Harvard Math Camp - Econometrics Ashesh Rambachan Summer 2018 Outline Random Experiments The sample space and events σ-algebra and measures Basic probability rules Conditional Probability

More information

Random variables (discrete)

Random variables (discrete) Random variables (discrete) Saad Mneimneh 1 Introducing random variables A random variable is a mapping from the sample space to the real line. We usually denote the random variable by X, and a value that

More information

Sampling Distributions

Sampling Distributions Sampling Distributions Mathematics 47: Lecture 9 Dan Sloughter Furman University March 16, 2006 Dan Sloughter (Furman University) Sampling Distributions March 16, 2006 1 / 10 Definition We call the probability

More information

EE514A Information Theory I Fall 2013

EE514A Information Theory I Fall 2013 EE514A Information Theory I Fall 2013 K. Mohan, Prof. J. Bilmes University of Washington, Seattle Department of Electrical Engineering Fall Quarter, 2013 http://j.ee.washington.edu/~bilmes/classes/ee514a_fall_2013/

More information

M378K In-Class Assignment #1

M378K In-Class Assignment #1 The following problems are a review of M6K. M7K In-Class Assignment # Problem.. Complete the definition of mutual exclusivity of events below: Events A, B Ω are said to be mutually exclusive if A B =.

More information

A random variable is a quantity whose value is determined by the outcome of an experiment.

A random variable is a quantity whose value is determined by the outcome of an experiment. Random Variables A random variable is a quantity whose value is determined by the outcome of an experiment. Before the experiment is carried out, all we know is the range of possible values. Birthday example

More information

Lecture 16. Lectures 1-15 Review

Lecture 16. Lectures 1-15 Review 18.440: Lecture 16 Lectures 1-15 Review Scott Sheffield MIT 1 Outline Counting tricks and basic principles of probability Discrete random variables 2 Outline Counting tricks and basic principles of probability

More information

Lecture 5. October 21, Department of Biostatistics Johns Hopkins Bloomberg School of Public Health Johns Hopkins University.

Lecture 5. October 21, Department of Biostatistics Johns Hopkins Bloomberg School of Public Health Johns Hopkins University. Lecture 5 Department of Biostatistics Johns Hopkins Bloomberg School of Public Health Johns Hopkins University October 21, 2007 1 2 3 4 5 6 7 1 Define conditional probabilities 2 Define conditional mass

More information

Notes 12 Autumn 2005

Notes 12 Autumn 2005 MAS 08 Probability I Notes Autumn 005 Conditional random variables Remember that the conditional probability of event A given event B is P(A B) P(A B)/P(B). Suppose that X is a discrete random variable.

More information

Stochastic Models of Manufacturing Systems

Stochastic Models of Manufacturing Systems Stochastic Models of Manufacturing Systems Ivo Adan Organization 2/47 7 lectures (lecture of May 12 is canceled) Studyguide available (with notes, slides, assignments, references), see http://www.win.tue.nl/

More information

Test Problems for Probability Theory ,

Test Problems for Probability Theory , 1 Test Problems for Probability Theory 01-06-16, 010-1-14 1. Write down the following probability density functions and compute their moment generating functions. (a) Binomial distribution with mean 30

More information

GEOMETRIC -discrete A discrete random variable R counts number of times needed before an event occurs

GEOMETRIC -discrete A discrete random variable R counts number of times needed before an event occurs STATISTICS 4 Summary Notes. Geometric and Exponential Distributions GEOMETRIC -discrete A discrete random variable R counts number of times needed before an event occurs P(X = x) = ( p) x p x =,, 3,...

More information

A Gentle Introduction to Gradient Boosting. Cheng Li College of Computer and Information Science Northeastern University

A Gentle Introduction to Gradient Boosting. Cheng Li College of Computer and Information Science Northeastern University A Gentle Introduction to Gradient Boosting Cheng Li chengli@ccs.neu.edu College of Computer and Information Science Northeastern University Gradient Boosting a powerful machine learning algorithm it can

More information

Expectation of Random Variables

Expectation of Random Variables 1 / 19 Expectation of Random Variables Saravanan Vijayakumaran sarva@ee.iitb.ac.in Department of Electrical Engineering Indian Institute of Technology Bombay February 13, 2015 2 / 19 Expectation of Discrete

More information

Gaussian random variables inr n

Gaussian random variables inr n Gaussian vectors Lecture 5 Gaussian random variables inr n One-dimensional case One-dimensional Gaussian density with mean and standard deviation (called N, ): fx x exp. Proposition If X N,, then ax b

More information

B4 Estimation and Inference

B4 Estimation and Inference B4 Estimation and Inference 6 Lectures Hilary Term 27 2 Tutorial Sheets A. Zisserman Overview Lectures 1 & 2: Introduction sensors, and basics of probability density functions for representing sensor error

More information

Generative Techniques: Bayes Rule and the Axioms of Probability

Generative Techniques: Bayes Rule and the Axioms of Probability Intelligent Systems: Reasoning and Recognition James L. Crowley ENSIMAG 2 / MoSIG M1 Second Semester 2016/2017 Lesson 8 3 March 2017 Generative Techniques: Bayes Rule and the Axioms of Probability Generative

More information

Analysis of Experimental Designs

Analysis of Experimental Designs Analysis of Experimental Designs p. 1/? Analysis of Experimental Designs Gilles Lamothe Mathematics and Statistics University of Ottawa Analysis of Experimental Designs p. 2/? Review of Probability A short

More information

Recap of Basic Probability Theory

Recap of Basic Probability Theory 02407 Stochastic Processes Recap of Basic Probability Theory Uffe Høgsbro Thygesen Informatics and Mathematical Modelling Technical University of Denmark 2800 Kgs. Lyngby Denmark Email: uht@imm.dtu.dk

More information

CHAPTER 6. 1, if n =1, 2p(1 p), if n =2, n (1 p) n 1 n p + p n 1 (1 p), if n =3, 4, 5,... var(d) = 4var(R) =4np(1 p).

CHAPTER 6. 1, if n =1, 2p(1 p), if n =2, n (1 p) n 1 n p + p n 1 (1 p), if n =3, 4, 5,... var(d) = 4var(R) =4np(1 p). CHAPTER 6 Solution to Problem 6 (a) The random variable R is binomial with parameters p and n Hence, ( ) n p R(r) = ( p) n r p r, for r =0,,,,n, r E[R] = np, and var(r) = np( p) (b) Let A be the event

More information

WYOMING COMMUNITY DEVELOPMENT AUTHORITY DISCLOSURE REPORT FOR THE 1994 INDENTURE SINGLE FAMILY HOUSING REVENUE BOND SERIES

WYOMING COMMUNITY DEVELOPMENT AUTHORITY DISCLOSURE REPORT FOR THE 1994 INDENTURE SINGLE FAMILY HOUSING REVENUE BOND SERIES WYOMING COMMUNITY DEVELOPMENT AUTHORITY DISCLOSURE REPORT FOR THE 1994 INDENTURE SINGLE FAMILY HOUSING REVENUE BOND SERIES 1994-1&2 THROUGH 2014-1, 2, 3, 4 & 5 AS OF SEPTEMBER 30, 2014 INDENTURE 007 IND.

More information

Your pure maths needs to be far stronger for S4 than in any other Statistics module. You must be strong on general binomial expansion from C4.

Your pure maths needs to be far stronger for S4 than in any other Statistics module. You must be strong on general binomial expansion from C4. OCR Statistics Module Revision Sheet The S exam is hour 0 minutes long. You are allowed a graphics calculator. Before you go into the exam make sureyou are fully aware of the contents of theformula booklet

More information

Machine Learning Srihari. Probability Theory. Sargur N. Srihari

Machine Learning Srihari. Probability Theory. Sargur N. Srihari Probability Theory Sargur N. Srihari srihari@cedar.buffalo.edu 1 Probability Theory with Several Variables Key concept is dealing with uncertainty Due to noise and finite data sets Framework for quantification

More information

MAS108 Probability I

MAS108 Probability I 1 BSc Examination 2008 By Course Units 2:30 pm, Thursday 14 August, 2008 Duration: 2 hours MAS108 Probability I Do not start reading the question paper until you are instructed to by the invigilators.

More information

We will briefly look at the definition of a probability space, probability measures, conditional probability and independence of probability events.

We will briefly look at the definition of a probability space, probability measures, conditional probability and independence of probability events. 1 Probability 1.1 Probability spaces We will briefly look at the definition of a probability space, probability measures, conditional probability and independence of probability events. Definition 1.1.

More information

Recap of Basic Probability Theory

Recap of Basic Probability Theory 02407 Stochastic Processes? Recap of Basic Probability Theory Uffe Høgsbro Thygesen Informatics and Mathematical Modelling Technical University of Denmark 2800 Kgs. Lyngby Denmark Email: uht@imm.dtu.dk

More information

Continuous Random Variables. What continuous random variables are and how to use them. I can give a definition of a continuous random variable.

Continuous Random Variables. What continuous random variables are and how to use them. I can give a definition of a continuous random variable. Continuous Random Variables Today we are learning... What continuous random variables are and how to use them. I will know if I have been successful if... I can give a definition of a continuous random

More information

Chapter 3: Random Variables 1

Chapter 3: Random Variables 1 Chapter 3: Random Variables 1 Yunghsiang S. Han Graduate Institute of Communication Engineering, National Taipei University Taiwan E-mail: yshan@mail.ntpu.edu.tw 1 Modified from the lecture notes by Prof.

More information

18.600: Lecture 7 Bayes formula and independence

18.600: Lecture 7 Bayes formula and independence 18.600 Lecture 7 18.600: Lecture 7 Bayes formula and independence Scott Sheffield MIT 18.600 Lecture 7 Outline Bayes formula Independence 18.600 Lecture 7 Outline Bayes formula Independence Recall definition:

More information

Discrete Markov Processes. 1. Introduction

Discrete Markov Processes. 1. Introduction Discree Markov Processes 1. Inroducion 1. Probabiliy Spaces and Random Variables Sample space. A model for evens: is a family of subses of such ha c (1) if A, hen A, (2) if A 1, A 2,..., hen A1 A 2...,

More information

ABSTRACT EXPECTATION

ABSTRACT EXPECTATION ABSTRACT EXPECTATION Abstract. In undergraduate courses, expectation is sometimes defined twice, once for discrete random variables and again for continuous random variables. Here, we will give a definition

More information

CSCE 478/878 Lecture 6: Bayesian Learning and Graphical Models. Stephen Scott. Introduction. Outline. Bayes Theorem. Formulas

CSCE 478/878 Lecture 6: Bayesian Learning and Graphical Models. Stephen Scott. Introduction. Outline. Bayes Theorem. Formulas ian ian ian Might have reasons (domain information) to favor some hypotheses/predictions over others a priori ian methods work with probabilities, and have two main roles: Naïve Nets (Adapted from Ethem

More information

12. Special Transformations 1

12. Special Transformations 1 12. Special Transformations 1 Projections Take V = R 3 and consider the subspace W = {(x, y,z) x y z = 0}. Then the map P:V V that projects every vector in R 3 orthogonally onto the plane W is a linear

More information

SDS 321: Introduction to Probability and Statistics

SDS 321: Introduction to Probability and Statistics SDS 321: Introduction to Probability and Statistics Lecture 10: Expectation and Variance Purnamrita Sarkar Department of Statistics and Data Science The University of Texas at Austin www.cs.cmu.edu/ psarkar/teaching

More information

CHAPTER - 3 Probability

CHAPTER - 3 Probability CHAPTER - 3 Probability 3.10 Glossary of probability Terms A B: An event which represents the happing of at least one of the events A and B. A B: An event which represents the simultaneous happening of

More information

Probability Theory and Applications

Probability Theory and Applications Probability Theory and Applications Videos of the topics covered in this manual are available at the following links: Lesson 4 Probability I http://faculty.citadel.edu/silver/ba205/online course/lesson

More information

Rapid Introduction to Machine Learning/ Deep Learning

Rapid Introduction to Machine Learning/ Deep Learning Rapid Introduction to Machine Learning/ Deep Learning Hyeong In Choi Seoul National University 1/32 Lecture 5a Bayesian network April 14, 2016 2/32 Table of contents 1 1. Objectives of Lecture 5a 2 2.Bayesian

More information

Bayesian Machine Learning

Bayesian Machine Learning Bayesian Machine Learning Andrew Gordon Wilson ORIE 6741 Lecture 4 Occam s Razor, Model Construction, and Directed Graphical Models https://people.orie.cornell.edu/andrew/orie6741 Cornell University September

More information

12 - The Tie Set Method

12 - The Tie Set Method 12 - The Tie Set Method Definitions: A tie set V is a set of components whose success results in system success, i.e. the presence of all components in any tie set connects the input to the output in the

More information

CSE 312 Final Review: Section AA

CSE 312 Final Review: Section AA CSE 312 TAs December 8, 2011 General Information General Information Comprehensive Midterm General Information Comprehensive Midterm Heavily weighted toward material after the midterm Pre-Midterm Material

More information

Section 4.2 Polynomial Functions of Higher Degree

Section 4.2 Polynomial Functions of Higher Degree Section 4.2 Polynomial Functions of Higher Degree Polynomial Function P(x) P(x) = a degree 0 P(x) = ax +b (degree 1) Graph Horizontal line through (0,a) line with y intercept (0,b) and slope a P(x) = ax

More information

4th IIA-Penn State Astrostatistics School July, 2013 Vainu Bappu Observatory, Kavalur

4th IIA-Penn State Astrostatistics School July, 2013 Vainu Bappu Observatory, Kavalur 4th IIA-Penn State Astrostatistics School July, 2013 Vainu Bappu Observatory, Kavalur Laws of Probability, Bayes theorem, and the Central Limit Theorem Rahul Roy Indian Statistical Institute, Delhi. Adapted

More information

Lecture 7. Bayes formula and independence

Lecture 7. Bayes formula and independence 18.440: Lecture 7 Bayes formula and independence Scott Sheffield MIT 1 Outline Bayes formula Independence 2 Outline Bayes formula Independence 3 Recall definition: conditional probability Definition: P(E

More information

Lecture Notes 1 Probability and Random Variables. Conditional Probability and Independence. Functions of a Random Variable

Lecture Notes 1 Probability and Random Variables. Conditional Probability and Independence. Functions of a Random Variable Lecture Notes 1 Probability and Random Variables Probability Spaces Conditional Probability and Independence Random Variables Functions of a Random Variable Generation of a Random Variable Jointly Distributed

More information

18.600: Lecture 7 Bayes formula and independence

18.600: Lecture 7 Bayes formula and independence 18.600: Lecture 7 Bayes formula and independence Scott Sheffield MIT Outline Bayes formula Independence Outline Bayes formula Independence Recall definition: conditional probability Definition: P(E F )

More information

Math Introduction to Probability. Davar Khoshnevisan University of Utah

Math Introduction to Probability. Davar Khoshnevisan University of Utah Math 5010 1 Introduction to Probability Based on D. Stirzaker s book Cambridge University Press Davar Khoshnevisan University of Utah Lecture 1 1. The sample space, events, and outcomes Need a math model

More information

Computational Logic. Standardization of Interpretations. Damiano Zanardini

Computational Logic. Standardization of Interpretations. Damiano Zanardini Computational Logic Standardization of Interpretations Damiano Zanardini UPM European Master in Computational Logic (EMCL) School of Computer Science Technical University of Madrid damiano@fi.upm.es Academic

More information

STATISTICS 1 REVISION NOTES

STATISTICS 1 REVISION NOTES STATISTICS 1 REVISION NOTES Statistical Model Representing and summarising Sample Data Key words: Quantitative Data This is data in NUMERICAL FORM such as shoe size, height etc. Qualitative Data This is

More information

Statistical Model Checking as Feedback Control

Statistical Model Checking as Feedback Control Statistical Model Checking as Feedback Control, MSc Vienna University of Technology Supervisor: Radu Grosu Co-supervisor: Ezio Bartocci Analysis of CPS: Challenges State-space explosion: Open, physical

More information

Continuous Random Variables and Continuous Distributions

Continuous Random Variables and Continuous Distributions Continuous Random Variables and Continuous Distributions Continuous Random Variables and Continuous Distributions Expectation & Variance of Continuous Random Variables ( 5.2) The Uniform Random Variable

More information

Topic 3: The Expectation of a Random Variable

Topic 3: The Expectation of a Random Variable Topic 3: The Expectation of a Random Variable Course 003, 2017 Page 0 Expectation of a discrete random variable Definition (Expectation of a discrete r.v.): The expected value (also called the expectation

More information

LOCUS. Definition: The set of all points (and only those points) which satisfy the given geometrical condition(s) (or properties) is called a locus.

LOCUS. Definition: The set of all points (and only those points) which satisfy the given geometrical condition(s) (or properties) is called a locus. LOCUS Definition: The set of all points (and only those points) which satisfy the given geometrical condition(s) (or properties) is called a locus. Eg. The set of points in a plane which are at a constant

More information

MATH 3MB3 FALL 2018 Univariate Sochastic 1

MATH 3MB3 FALL 2018 Univariate Sochastic 1 MATH 3MB3 FALL 2018 Univariate Sochastic 1 What we are now interested in is where different parameters or some part of the model is allowed to have it s value change over time, and this change deps on

More information

Lecture 2: Probability, conditional probability, and independence

Lecture 2: Probability, conditional probability, and independence Lecture 2: Probability, conditional probability, and independence Theorem 1.2.6. Let S = {s 1,s 2,...} and F be all subsets of S. Let p 1,p 2,... be nonnegative numbers that sum to 1. The following defines

More information

Lecture 4. Selected material from: Ch. 6 Probability

Lecture 4. Selected material from: Ch. 6 Probability Lecture 4 Selected material from: Ch. 6 Probability Example: Music preferences F M Suppose you want to know what types of CD s males and females are more likely to buy. The CD s are classified as Classical,

More information