PPT-Quick Review Probability Theory

Mixture of Transparencies created by Dr Eick and Dr Russel Reasoning and Decision Making Under Uncertainty Quick Review Probability Theory Bayes Theorem and Naïve Bayesian Systems Bayesian Belief Networks. Through this class we will be relying on concepts from probability theory for deriving machine learning algorithms These notes attempt to cover the basics of probability theory at a level appropriate for CS 229 The mathematical theory of probability Continued Fractions. Lisa Lorentzen. Norwegian University of Science and Technology. Continued fraction:. Convergence:. Möbius. transformations:. Convergence:. Catch:. L (1986). General convergence:. ENGR 4323/5323. Digital and Analog Communication. Engineering and Physics. University of Central Oklahoma. Dr. Mohamed Bingabr. Chapter Outline. Concept of Probability. Random Variables. Statistical Averages (MEANS). 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.. 4. Introduction. (slide 1 of 3). A key . aspect of solving real business problems is dealing appropriately with uncertainty.. This involves recognizing explicitly that uncertainty exists and using quantitative methods to model uncertainty.. William W. Cohen. Machine Learning 10-605. Jan 19 2012. Probabilistic and Bayesian Analytics. Andrew W. Moore. School of Computer Science. Carnegie Mellon University. www.cs.cmu.edu/~awm. awm@cs.cmu.edu. Conditional Probability. Conditional Probability: . A probability where a certain prerequisite condition has already been met.. Conditional Probability Notation. The probability of Event A, given that Event B has already occurred, is expressed as P(A | B).. The quick brown fox jumps over the lazy dog. The quick brown fox jumps over the lazy dog. The quick brown fox jumps over the lazy dog. The quick brown fox jumps over the lazy dog. . The quick brown fox jumps over the lazy dog. The quick brown fox jumps over the lazy dog. The quick brown fox jumps over the lazy dog. The quick brown fox jumps over the lazy dog.. 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 . A value between zero and one that describe the relative possibility(change or likelihood) an event occurs.. The MEF announces that in 2012 the change Cambodia economic growth rate is equal to 7% is 80%.. 4. Compute the number of combinations of . n. individuals taken . k. at a time.. Use . combinations to calculate probabilities.. Use . the multiplication counting principle and combinations to calculate probabilities.. The quick brown fox jumps over the lazy dog. The quick brown fox jumps over the lazy dog. The quick brown fox jumps over the lazy dog. The quick brown fox jumps over the lazy dog. The quick brown fox jumps over the lazy dog. . and . RATIONALITY – Some general comments. 2. 3. Decision Theory. Formidable foundations. Probability and reasoning about the future. Rational decision making. Deeply rooted in the Enlightenment. Major leaps in the mid-20. 4. Interpret probability as a long-run relative frequency. . Dispel . common myths about randomness.. Use . simulation to model chance behavior.. Randomness, Probability, and Simulation. Randomness, Probability, and Simulation.