PPT-Use the Counting Rules to compute Probabilities

2 of these and 4 of those A classic type of problem You have various subgroups When you pick 6 what is the probability that you get 2 of this group and 4 of that group Jellybeans 30 red 30 yellow 40 other. Set 8. PCFGs and the CKY Algorithm. PCFGs. We saw how CFGs can model English (sort of). Probabilistic. . CFGs put weights on the production rules. NP -> DET NN . with probability 0.34. NP -> . Need to write what you know as propositional formulas. Theorem proving will then tell you whether a given new sentence will hold given what you know. Three kinds of queries. Is my . knowledgebase . consistent? (i.e. is there at least one world where everything I know is true?) . Structure and Concepts. D-Separation . How do they compute probabilities? . How to design BBN . using simple examples. Other capabilities of Belief Network. Netica Demo . short!. Example 2. BN. : probability of a variable only depends on its direct successors; . 1. 2. Probability. 2-1 Sample Spaces & Events. 2-1.1 Random Experiments. 2-1.2 Sample Spaces. 2-1.3 Events. 2-1.4 Count Techniques. 2-2 Interpretations & Axioms of Probability. Chapter 14: . From . Randomness . to Probability . Unit 4. Are you at Lloyd Christmas’s level?. http://www.youtube.com/watch?v=qULSszbA-Ek. . Introduction to Probability…. A . random phenomenon . We have only examined knowledge that is true/false or truth preserving, but the world is full of uncertainty. we need mechanisms to reason with that uncertainty. We find two forms of uncertainty. unsure input. Representations generally treat knowledge as . fact. Knowledge and the use of the knowledge brings with it a degree of uncertainty . how do we represent and reason with uncertainty?. Four forms of uncertainty. ESSENTIAL IDEAS OF PROBABILITY. ESSENTIAL IDEAS OF PROBABILITY Page . 1. MEASURING PROBABILITIES —. The . PROBABILITY. of an . event . is a measurement of . how possible. , or . how likely. it is . Pair . Gross . Ream . 12. 2. 144. 500. Counting M&M’s. Example 1:How many M&M’s are in one party-sized bag? . Given on the bag… . 1 bag = 1190.7g . We want… . # M&M’s per bag . Professor Daniel Leeds. Representing sequence data. Spoken language. DNA sequences. Daily stock values. Example: spoken language. F?r. . plu. ? . fi?e. . i. s nine. Between F and r expect a vowel: “aw”, “. Algebra 2. Chapter 10. This Slideshow was developed to accompany the textbook. Larson Algebra 2. By Larson. , R., Boswell, L., . Kanold. , T. D., & Stiff, L. . 2011 . Holt . McDougal. Some examples and diagrams are taken from the textbook.. 2 of these and 4 of those. A classic type of problem. You have various subgroups.. When you pick 6, what is the probability that you get 2 of this group and 4 of that group?. Jellybeans: . 30 . red, 30 yellow, 40 other. Reasoning with Uncertainty We have only examined knowledge that is true/false or truth preserving, but the world is full of uncertainty we need mechanisms to reason with that uncertainty We find two forms of uncertainty DALMATIAN. The policeman ate the spaghetti with a nice sauce.. The policeman ate the spaghetti with a nice fork.. Context matters. Today. An influential early account of how/why context matters in perception/recognition that depends on .