PPT-Reasoning under Uncertainty: Conditional Prob.,

Bayes and Independence Computer Science cpsc322 Lecture 25 Textbook Chpt 61312 Nov 5 2012 Lecture Overview Recap Semantics of Probability Marginalization Conditional Probability. Table 4: Performance on Tasks by the symptom subgroups – means and (sds). Negative signs (n=10) Thought disorder (n=10) Persecutory delusions (n=16) Non-persecutory delusions (n=8) Remit 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. DOI: 10.1142/S0218488510006696BAYESIAN NETWORK REASONING WITH UNCERTAIN EVIDENCESYUN PENG University of Maryland Baltimore County, Computer Science and Electrical Engineering, 1000 Hilltop Circle, Bal Human reasoning is . non-monotonic. or . defeasible:. Additional premises can render otherwise acceptable inferences unacceptable (Byrne, 1989. ). Example of . suppression effects. . (. i.e., acceptance . Heng. . Ji. jih@rpi.edu. 04/05, 04/08, 2016. Bayesian networks. More commonly called . graphical models. A way to depict conditional independence relationships between random variables. A compact . specification of full joint . Bayesian networks intro. Jim Little. Uncertainty 4. November 7, 2014. Textbook §6.3, 6.3.1, 6.5, 6.5.1, 6.5.2. Lecture . Overview. Recap: marginal and conditional independence. Bayesian Networks Introduction. Using elimination of all false statements to prove a statement true. Negation. The negation of a statement has the opposite truth value to the statement.. Example:. The statement “Cedar Rapids is the capital of Iowa” is false.. . Decision. . Theory. Wolfgang Spohn. Multi-. disciplinary. . approaches. . to. . reasoning. . with. . imperfect. . information. . and. . knowledge. . – . a . synthesis. . and. a . roadmap. Pavle. . Valerjev. Department of psychology. University of . Zadar. Thinking. Psychology of thinking. Reasoning. Problem solving. Judgment and decision making. Undirected thinking (Gilhooly, 1996). Cognitive approach: Thinking as . Chapter 13. Uncertainty in the World. An agent can often be uncertain about the state of the world/domain since there is often ambiguity and uncertainty. Plausible/. probabilistic inference. I’ve got this evidence; what’s the chance that this conclusion is true?. SHARPn . NLP. Presentation to . SHARPn . Summit “. Secondary Use. ”. June . 11-12, 2012 . Cheryl Clark, PhD. MITRE Corporation . Negation. : . event has not occurred . or . entity does not . exist . Reasoning and Proof Geometry Chapter 2 This Slideshow was developed to accompany the textbook Larson Geometry By Larson , R., Boswell, L., Kanold , T. D., & Stiff, L. 2011 Holt McDougal Some examples and diagrams are taken from the textbook. Probability Space of Two Die. σ-. Algebra (. ℱ. ). Sample Space (Ω). [...]. E5={(1,4),(2,3),(3,2),(4,1)}. [...]. Probability Measure Function (P). P. E5. 0.11. Probability Measure Function (P). . High-dimensional Data Analysis. Adel Javanmard. Stanford University. 1. What is . high. -dimensional data?. Modern data sets are both massive and fine-grained.. 2. # Features (variables) > . # . Observations (Samples).