PPT-Philosophy 104 Probability and Bayes

Theorem Common fallacies of probability The Gamblers Fallacy Is assuming that the odds of a single truly random event are affected in any way by previous iterations of the same or other truly random event. Oliver . Schulte. Bayesian Networks. Environment Type: Un. certain. Artificial Intelligence a modern approach. 2. Fully Observable. Deterministic. Certainty: Search. Uncertainty. no. yes. yes. no. Motivation. for Beginners. Presenters: Shuman . ji. & Nick Todd. Statistic Formulations.. P(A): probability of event A occurring. P(A|B): probability of A occurring given B occurred. P(B|A): probability of B occurring given A occurred. for beginners. Methods for . dummies. 27 February 2013. Claire Berna. Lieke de Boer. Bayes . rule. Given . marginal probabilities . p(A. ), p(B. ), . and . the . joint probability p(A,B. ), . we can . CLASSIFIER. 1. ACM Student Chapter,. Heritage Institute of Technology. 10. th. February, 2012. SIGKDD Presentation by. Anirban. . Ghose. Parami. Roy. Sourav. . Dutta. CLASSIFICATION . What is it?. Random variables, events. Axioms of probability. Atomic events. Joint and marginal probability distributions. Conditional probability distributions. Product . rule, chain rule. Independence and conditional independence. Hadoop. ). . COSC 526 Class 3. Arvind Ramanathan. Computational Science & Engineering Division. Oak Ridge National Laboratory, Oak Ridge. Ph. : 865-576-7266. E-mail: . ramanathana@ornl.gov. . Hadoop. for Beginners. Presenters: Shuman . ji. & Nick Todd. Statistic Formulations.. P(A): probability of event A occurring. P(A|B): probability of A occurring given B occurred. P(B|A): probability of B occurring given A occurred. Lecture 6 . (Largely drawn from Kleinberg book). Following the crowd. We are often influenced by others. Opinions. Political positions. Fashion. Technologies to use. Why do we sometimes imitate the choices of others even if information suggests otherwise?. . Chowdhury. & Peter . Smittenaar. Methods for Dummies 2011. Dec 7. th. 2011. A disease occurs in 0.5% of population. A diagnostic test gives a positive result. in 99% of people that have the disease. 2. Naïve Bayes Classifier. We will start off with . some mathematical background. But first we start with some. visual intuition. .. Thomas Bayes. 1702 - 1761. . 3. Antenna Length. 10. 1. 2. 3. 4. Arunkumar. . Byravan. CSE 490R – Lecture 3. Interaction loop. Sense: . Receive sensor data and estimate “state”. Plan:. Generate long-term plans based on state & goal. Act:. Apply actions to the robot. Let B. 1. , B. 2. , …, B. N. be mutually exclusive events whose union equals the sample space S. We refer to these sets as a partition of S.. An event A can be represented as:. Since B. 1. , B. 2. * 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.. Exercise . 1. Bayes Theorem. 2. Guide to Intelligent Data Science . Second Edition, 2020. Bayes Theorem. 3. The conditional probability . , hypothesis . is true given event . : Probability of hypothesis .