PPT-Ch 17 – Probability Models

Objective We will learn the characteristics of Bernoulli trials and how to calculate probabilities based on geometric models Get out paper for notes Closing task I will complete and exit ticket in which I calculate the geometric probabilities of four events. 14. th. TRB Transportation Planning Applications Conference. May 2013. Thomas Adler, RSG. Michael Doherty, URS. Jack Klodzinski, URS . The Problem – Transit New Starts Forecasts. Travel demand model forecasts are not always accurate. Tamara L Berg. CSE 595 Words & Pictures. Announcements. HW3 . online tonight. Start thinking about project ideas . Project . proposals in class Oct 30 . . Come to office hours . Oct. 23-25 . to discuss . 1. Topic Overview. Introduction to binary choice models . The . Linear Probability . model . (LPM). The . Probit . model. The . Logit . model . 2. Introduction. In . some cases the outcome of interest (. Machine Learning @ CU. Intro courses. CSCI 5622: Machine Learning. CSCI 5352: Network Analysis and Modeling. CSCI 7222: Probabilistic Models. Other courses. cs.colorado.edu/~mozer/Teaching/Machine_Learning_Courses. Jennifer Trueblood, James . Yearsley. , Peter . Kvam. , Jerome . Busemeyer. , and . Zheng. (Joyce) Wang. Supported by NSF . (SES 0818277, 1153846, 1326275) & AFOSR (FA9550-12-1-00397) . Today’s agenda. Lecture . 5. Albert . Gatt. LIN3022 -- Natural Language Processing. In today’s lecture. We take a look at . n-gram. . language models. Simple, probabilistic models of linguistic sequences. LIN3022 -- Natural Language Processing. calculus. 1 ≥ . Pr. (h) ≥ 0. If e deductively implies h, then Pr(h|e) = 1. .. (disjunction rule) If h and g are mutually exclusive, then . Pr. (h or g) = . Pr. (h) . Pr. (g). (disjunction rule) If h and g are . Probability Terminology. Classical Interpretation. : Notion of probability based on equal likelihood of individual possibilities (coin toss has 1/2 chance of Heads, card draw has 4/52 chance of an Ace). Origins in games of chance.. 3.1 . The Concept of Probability. 3.2 . Sample Spaces and Events. 3.3 . Some Elementary Probability Rules. 3.4 . Conditional Probability and Independence. 3.5 . Bayes’ Theorem. 3-. 2. Probability Concepts. AP Statistics B. Overview of Chapter 17. Two new models: Geometric model, and the Binomial model. Yes, the binomial model involves Pascal’s triangles that (I hope) you learned about in Algebra 2. Use the geometric model whenever you want to find how many events you have to have before a “success”. Probability is used all of the time in real life. Gambling . Sports. Weather. Insurance. Medical Decisions. Standardized Tests. And others. Definition of Probability. “The . likelihood of something . Slide . 2. Probability - Terminology. Events are the . number. of possible outcome of a phenomenon such as the roll of a die or a fillip of a coin.. “trials” are a coin flip or die roll. Slide . Sixth Edition. Douglas C. Montgomery George C. . Runger. Chapter 2 Title and Outline. 2. 2. Probability. 2-1 Sample Spaces and Events . 2-1.1 Random Experiments. 2-1.2 Sample Spaces . calculus. 1 ≥ . Pr. (h) ≥ 0. If e deductively implies h, then Pr(h|e) = 1. .. (disjunction rule) If h and g are mutually exclusive, then . Pr. (h or g) = . Pr. (h) + . Pr. (g). (disjunction rule) If h and g are .