PPT-Bayes Models Outline I. Conditional independence

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. Pieter . Abbeel. UC Berkeley EECS. Many slides adapted from . Thrun. , . Burgard. and Fox, Probabilistic Robotics. TexPoint fonts used in EMF. . Read the TexPoint manual before you delete this box.: . Bayes. Nets. Lecture 4. Getting a Full Joint Table Entry from a Bayes Net. Recall:. A . table entry for . X. 1 . = x. 1. ,…,. X. n. . = . x. n. . is simply P(. x. 1. ,…,. x. n. ) which can be calculated based on the . Classification. Naïve . Bayes. . c. lassifier. Nearest-neighbor classifier. Eager . vs. Lazy learners. Eager learners: learn the model as soon as the training data becomes available. Lazy learners: delay model-building until testing data needs to be classified. 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?. Pieter . Abbeel. UC Berkeley EECS. Many slides adapted from . Thrun. , . Burgard. and Fox, Probabilistic Robotics. TexPoint fonts used in EMF. . Read the TexPoint manual before you delete this box.: . “I revoke my will if [condition] occurs.”. 2. Implied conditional revocation. (Dependent Relative Revocation). Fact Pattern:. 1. Testator executed valid Will 1.. 2. Testator validly revoked Will 1.. 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?. Generative vs. Discriminative models. Christopher Manning. Introduction. So far we’ve looked at “generative models”. Language models, Naive Bayes. But there is now much use of conditional or discriminative probabilistic models in NLP, Speech, IR (and ML generally). 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. Human and Machine Learning. Mike . Mozer. Department of Computer Science and. Institute of Cognitive Science. University of Colorado at Boulder. Today’s Plan. Hand back Assignment 1. More fun stuff from motion perception model. DATA ULANG PMP (PENERIMA MANFAAT PENSIUN). Oleh. Novia Ervianti & Wendi Wirasta ST., MT.. ervianti.novia@fellow.lpkia.ac.id. & wendiwirasta@fellow.ac.id. STMIK & POLITEKNIK LPKIA BANDUNG. Bayes. and Independence. Computer Science cpsc322, Lecture 25. (Textbook . Chpt. . 6.1.3.1-2). Nov, 5, 2012. Lecture Overview. Recap Semantics of Probability. Marginalization. Conditional Probability. Outline. I. Semantics. * Figures are either from the . textbook site. or by the instructor.. II. Network construction. III. Conditional independence relations. I. Knowledge in an Uncertain Domain. .