computer vision: models, learning and inference chapter 2 introduction to probability please send...
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Computer vision: models, learning and inference
Chapter 2 Introduction to probability
Please send errata to [email protected]
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Random variables
• A random variable x denotes a quantity that is uncertain
• May be result of experiment (flipping a coin) or a real world measurements (measuring temperature)
• If observe several instances of x we get different values
• Some values occur more than others and this information is captured by a probability distribution
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Discrete Random Variables
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Continuous Random Variable
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Joint Probability
• Consider two random variables x and y• If we observe multiple paired instances, then some
combinations of outcomes are more likely than others
• This is captured in the joint probability distribution• Written as Pr(x,y)• Can read Pr(x,y) as “probability of x and y”
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Joint Probability
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MarginalizationWe can recover probability distribution of any variable in a joint distribution
by integrating (or summing) over the other variables
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MarginalizationWe can recover probability distribution of any variable in a joint distribution
by integrating (or summing) over the other variables
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MarginalizationWe can recover probability distribution of any variable in a joint distribution
by integrating (or summing) over the other variables
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MarginalizationWe can recover probability distribution of any variable in a joint distribution
by integrating (or summing) over the other variables
Works in higher dimensions as well – leaves joint distribution between whatever variables are left
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Conditional Probability
• Conditional probability of x given that y=y1 is relative propensity of variable x to take different outcomes given that y is fixed to be equal to y1.
• Written as Pr(x|y=y1)
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Conditional Probability• Conditional probability can be extracted from joint probability• Extract appropriate slice and normalize
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Conditional Probability
• More usually written in compact form
• Can be re-arranged to give
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Conditional Probability
• This idea can be extended to more than two variables
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Bayes’ RuleFrom before:
Combining:
Re-arranging:
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Bayes’ Rule Terminology
Posterior – what we know about y after seeing x
Prior – what we know about y before seeing x
Likelihood – propensity for observing a certain value of x given a certain value of y
Evidence –a constant to ensure that the left hand side is a valid distribution
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Independence• If two variables x and y are independent then variable x tells
us nothing about variable y (and vice-versa)
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Independence• If two variables x and y are independent then variable x tells
us nothing about variable y (and vice-versa)
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Independence• When variables are independent, the joint factorizes into a
product of the marginals:
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ExpectationExpectation tell us the expected or average value of some function f [x] taking into account the distribution of x
Definition:
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ExpectationExpectation tell us the expected or average value of some function f [x] taking into account the distribution of x
Definition in two dimensions:
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Expectation: Common Cases
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Expectation: Rules
Rule 1:
Expected value of a constant is the constant
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Expectation: Rules
Rule 2:
Expected value of constant times function is constant times expected value of function
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Expectation: Rules
Rule 3:
Expectation of sum of functions is sum of expectation of functions
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Expectation: Rules
Rule 4:
Expectation of product of functions in variables x and y is product of expectations of functions if x and y are independent
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Conclusions
Computer vision: models, learning and inference. ©2011 Simon J.D. Prince
• Rules of probability are compact and simple
• Concepts of marginalization, joint and conditional probability, Bayes rule and expectation underpin all of the models in this book
• One remaining concept – conditional expectation – discussed later