PPT-Joint, Marginal, and Conditional Distributions

Section 08 Joint distribution of X and Y defined over a twodimensional region Discrete Continuous X and Y may be independent or dependent   CDF of a joint distribution Discrete Continuous. A brief digression back to . joint probability: . i.e. . both events . O. . and. . H. occur. .  . Again, we can express joint probability in terms of their separate conditional and unconditional probabilities. distributions; marginal and conditional distributions; independent random variables; mathematical expectation; mean and variance; binomial, Poisson and normal distributions; sum of independent rand Independence. Jim Little. Uncertainty . 3. Nov 5, 2014. Textbook §6.2. Lecture . Overview. Recap. Conditioning & Inference by Enumeration. Bayes Rule & The Chain Rule. Independence. Marginal Independence. We considered estimating the mean alone or the variance alone. This lecture deals with . estimating both together. We will consider various techniques of obtaining statistics from these distributions.. Computer Science cpsc322, Lecture 26. (Textbook . Chpt. 6.1-2). Nov. , . 2013. Lecture Overview. Recap with Example. Marginalization. Conditional Probability. Chain Rule. Bayes. ' Rule. Marginal Independence. Applied Statistics and Probability for Engineers. Sixth Edition. Douglas C. Montgomery George C. . Runger. Chapter 5 Title and Outline. 2. 5. Joint Probability Distributions. 5-1 Two or More Random Variables. 1. 5. Joint Probability Distributions. 5-1 Two or More Random Variables. 5-1.1 Joint Probability Distributions. 5-1.2 Marginal Probability Distributions. 5-1.3 Conditional Probability Distributions. Section 1.1. Analyzing Categorical Data. The Practice of Statistics, 4. th. edition - For AP*. STARNES, YATES, MOORE. Chapter 1. Exploring Data. Introduction. :. . Data Analysis: Making Sense of Data. A bag contains 4 red and 2 yellow marbles. A marble is selected, kept out of the bag, and another marble is selected. Find each conditional probability of selecting the second marble.. 1.. . P. (red | red). .  . .  . .  . .  . .  . .  . Announcements. Assignments:. HW9 (written). Due Tue 4/2, 10 pm. Optional Probability (online). Midterm:. Mon 4/8, in-class. Course Feedback:. See Piazza post for mid-semester survey. Bayes nets encode joint distributions as product of conditional distributions on each variable:. P. (. X. 1. ,..,X. n. ) = . . i. . P. (. X. i. . | . Parents. (. X. i. )). P(B). true. false. 0.001. Bayes. and Independence. Computer Science cpsc322, Lecture 25. (Textbook . Chpt. . 6.1.3.1-2). Nov, 5, 2012. Lecture Overview. Recap Semantics of Probability. Marginalization. Conditional Probability. Probability. Slides by Svetlana Lazebnik, 9/2016. Modified by Mark Hasegawa-Johnson, 2/2019. Outline. Motivation: Why use probability?. Review of Key Concepts. Outcomes, Events. Joint, Marginal, and Conditional. * Figures are from the . textbook site. .. II. Naïve Bayes model. III. Revisiting the . wumpus. world. I. Combining Evidence. What happens when we have two or more pieces of evidences?. . Suppose we know the full joint distribution..