1 / 6

Conditional Probability & Conditional Expectation

Conditional Probability & Conditional Expectation. Conditional distributions Computing expectations by conditioning Computing probabilities by conditioning. Discrete conditional distributions. Given a joint probability mass function the conditional pmf of X given that Y = y is

hanh
Download Presentation

Conditional Probability & Conditional Expectation

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Conditional Probability & Conditional Expectation Conditional distributions Computing expectations by conditioning Computing probabilities by conditioning Chapter 3

  2. Discrete conditional distributions Given a joint probability mass function the conditional pmf of X given that Y = y is The conditional expectation of X given Y = y is Chapter 3

  3. Continuous conditional distributions Given a joint probability density function the conditional pdf of X given that Y = y is This may seem nonsensical since P{Y = y} = 0 if Y is continuous. Interpret as the conditional probability that X is between x and x + dx given that Y is between y and y + dy. The conditional expectation of X given Y = y is Chapter 3

  4. Computing Expectations by Conditioning Suppose we want to know E[X] but the distribution of X is difficult to find. However, knowing Y gives us some useful information about X – in particular, we know E[X|Y=y]. • E[X|Y=y] is a number but E[X|Y] is a random variable since Y is a random variable. • We can find E[X] from If Y is discrete then If Y is continuous then Chapter 3

  5. Computing Probabilities by Conditioning Suppose we want to know the probability of some event, E (this event could describe a set of values for a random variable). Knowing Y gives us some useful information about whether or not E occurred. Define an indicator random variable Then P(E) = E[X], P(E|Y = y) = E[X|Y = y] So we can find P(E) from Chapter 3

  6. Strategies for Solving Problems • What piece of information would help you find the probability or expected value you seek? • When dealing with a sequence of choices, trials, etc., condition on the outcome of the first one Can also find variance by conditioning: Chapter 3

More Related