'Posterior probabilities' presentation slideshows

Bayesian Learning

Bayesian Learning. Bayesian Learning. Probabilistic approach to inference Assumption Quantities of interest are governed by probability distribution Optimal decisions can be made by reasoning about probabilities and observations

930 views • 64 slides

Christopher M. Bishop

Latent Variables, Mixture Models and EM. Christopher M. Bishop. Microsoft Research, Cambridge. BCS Summer School Exeter, 2003. Overview. K-means clustering Gaussian mixtures Maximum likelihood and EM Latent variables: EM revisited Bayesian Mixtures of Gaussians. Old Faithful.

708 views • 55 slides

Discrete Choice Modeling

851 views • 54 slides

FEATURE EXTRACTION AND FUSION TECHNIQUES FOR PATCH-BASED FACE RECOGITION

FEATURE EXTRACTION AND FUSION TECHNIQUES FOR PATCH-BASED FACE RECOGITION. Berkay Topcu Sabanc? University, 2009. Out line. Introduction Feature Extraction Dimensionality Reduction Normalization Methods Patch-Based Face Recognition Patch-Based Methods

718 views • 46 slides

CSC2515 Fall 2007 Introduction to Machine Learning Lecture 5: Mixture models, EM and variational inference

CSC2515 Fall 2007 Introduction to Machine Learning Lecture 5: Mixture models, EM and variational inference. All lecture slides will be available as .ppt, .ps, & .htm at www.cs.toronto.edu/~hinton Many of the figures are provided by Chris Bishop

697 views • 45 slides

DECISION ANALYSIS

DECISION ANALYSIS. Decision Analysis. Structuring the Decision Problem / Developing a Decision Strategy Decision Making Without Probabilities Decision Making with Probabilities Expected Value of Perfect Information Decision Analysis with Sample Information

847 views • 44 slides

Effective Estimation of Posterior Probabilities: - Explaining the Accuracy of Randomized Decision Tree Approaches

Effective Estimation of Posterior Probabilities: - Explaining the Accuracy of Randomized Decision Tree Approaches. Wei Fan Ed Greengrass Joe McCloskey Philip S. Yu Kevin Drummey. Example Simple Decision Tree Method. Construction: at each node, a feature is chosen randomly Discrete:

524 views • 34 slides

445 views • 34 slides

Towards Robustness in Query Auditing

Towards Robustness in Query Auditing. Shubha U. Nabar Stanford University VLDB 2006 Joint Work With B. Marthi, K. Kenthapadi, N. Mishra, R. Motwani. Data Mining vs Privacy. Large amount of data available in digital form Statisticians query data to mine useful trends

426 views • 31 slides

Reid D. Landes Trey Spencer Ian A. Zelaya

A Bayesian random coefficient nonlinear regression for a split-plot experiment for detecting differences in the half-life of a compound. Reid D. Landes Trey Spencer Ian A. Zelaya. Outline. Background and objective Description of experiment Classical analysis plan Bayesian analysis plan

511 views • 28 slides

Multiple Alignment of Citation Sentences with Conditional Random Fields and Posterior Decoding

Multiple Alignment of Citation Sentences with Conditional Random Fields and Posterior Decoding. Ariel Schwartz, Anna Divoli, and Marti Hearst University of California, Berkeley Supported in part by NSF DBI 0317510. Bioscience literature. Rich, complex and fast growing.

336 views • 27 slides

Decision Theory

Decision Theory. Professor Ahmadi. Learning Objectives. Structuring the decision problem and decision trees Types of decision making environments: Decision making under uncertainty when probabilities are not known Decision making under risk when probabilities are known

473 views • 26 slides

Mixture Models of Choice under Risk

Mixture Models of Choice under Risk. Peter G. MOFFATT University of East Anglia, Norwich, United Kingdom. (with Anna CONTE and John D. HEY, both LUISS, Rome). Estimating preference functionals for choice under risk from the choice behaviour of individuals. Data: Hey (2001, Exp. Ec. ).

339 views • 25 slides

Uncertainty

Uncertainty. Chapter 13. Outline. Uncertainty Probability Syntax and Semantics Inference Independence and Bayes' Rule. Uncertainty. Let action A t = leave for airport t minutes before flight Will A t get me there on time? Problems:

420 views • 24 slides

Shape Classification Based on Skeleton Path Similarity

463 views • 22 slides

HUMAN AND SYSTEMS ENGINEERING:

HUMAN AND SYSTEMS ENGINEERING:. Confidence Measures Based on Word Posteriors and Word Graphs. Sridhar Raghavan 1 and Joseph Picone 2 Graduate research assistant 1 , Human and Systems Engineering Professor 2 ,Electrical and Computer Engineering

327 views • 21 slides

Bayesian Statistics

Bayesian Statistics. Not in FPP. The Frequentist paradigm. Defines probability as a long-run frequency independent, identical trials Looks at parameters (i.e., the true mean of the population, the true probability of heads) as fixed quantities

269 views • 13 slides

Auxiliary particle filtering: recent developments

259 views • 11 slides

Steven J. Wagstaff 1 , Ilse Breitwieser 1 , & M otomi Ito 2

Evolution and biogeography of Pleurophyllum (Asteraceae), a small genus of megaherbs endemic to the Subantarctic Islands. Steven J. Wagstaff 1 , Ilse Breitwieser 1 , & M otomi Ito 2 1 Allan Herbarium, Landcare Research, Lincoln 7640, New Zealand.

209 views • 5 slides

Violation of NB Assumptions

147 views • 3 slides