'Predictive distribution' presentation slideshows

Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning. Chapter 1: Introduction. Example. Handwritten Digit Recognition. Polynomial Curve Fitting . Sum-of-Squares Error Function. 0 th Order Polynomial. 1 st Order Polynomial. 3 rd Order Polynomial. 9 th Order Polynomial. Over-fitting.

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Pattern Recognition and Machine Learning

1.27k views • 57 slides

Pattern Recognition and Machine Learning

1.21k views • 57 slides

Machine Learning CMPT 726 Simon Fraser University

Machine Learning CMPT 726 Simon Fraser University. CHAPTER 1: INTRODUCTION. Outline. Comments on general approach. Probability Theory. Joint, conditional and marginal probabilities. Random Variables. Functions of R.V.s Bernoulli Distribution (Coin Tosses).

630 views • 52 slides

Variational Bayes 101

Variational Bayes 101. The Bayes scene. Exact averaging in discrete/small models (Bayes networks) Approximate averaging: - Monte Carlo methods - Ensemble/mean field - Variational Bayes methods. Variational-Bayes .org MLpedia Wikipedia. ISP Bayes:

822 views • 48 slides

Bayesian Inconsistency under Misspecification

540 views • 41 slides

Variational Inference for Dirichlet Process Mixture

Variational Inference for Dirichlet Process Mixture. Applied Bayesian Nonparametrics Special Topics in Machine Learning Brown University CSCI 2950-P, Fall 2011. Daniel Klein and Soravit Beer Changpinyo October 11, 2011. Motivation.

715 views • 39 slides

Additional Topics in Prediction Methodology

Additional Topics in Prediction Methodology. Introduction. Predictive distribution for random variable Y 0 is meant to capture all the information about Y 0 that is contained in Y n .

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Karl Claxton and Tony Ades

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Taking Uncertainty Into Account: Bias Issues Arising from Uncertainty in Risk Models

Taking Uncertainty Into Account: Bias Issues Arising from Uncertainty in Risk Models. John A. Major, ASA Guy Carpenter & Company, Inc. Example: Exponential Distribution. N=20 observations T = sample mean; l =1 true mean MLE EP curve: q-exceedance point (PML, VaR) X .01 = 4.605 actual.

298 views • 21 slides

Summarized by Yung-Kyun Noh Biointelligence Laboratory, Seoul National University

Ch 3. Linear Models for Regression (1/2) Pattern Recognition and Machine Learning, C. M. Bishop, 2006. Summarized by Yung-Kyun Noh Biointelligence Laboratory, Seoul National University http://bi.snu.ac.kr/. Contents. 3.1 Linear Basis Function Models

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Learning In Bayesian Networks

310 views • 18 slides

Previously summarized by Yung-Kyun Noh Updated and presented by Rhee, Je-Keun

Ch 3. Linear Models for Regression (2/2) Pattern Recognition and Machine Learning, C. M. Bishop, 2006. Previously summarized by Yung-Kyun Noh Updated and presented by Rhee, Je-Keun Biointelligence Laboratory, Seoul National University http://bi.snu.ac.kr/. Contents.

225 views • 14 slides

Previously summarized by Yung-Kyun Noh Updated and presented by Rhee, Je-Keun

Ch 3. Linear Models for Regression (2/2) Pattern Recognition and Machine Learning, C. M. Bishop, 2006. Previously summarized by Yung-Kyun Noh Updated and presented by Rhee, Je-Keun Biointelligence Laboratory, Seoul National University http://bi.snu.ac.kr/. Contents.

142 views • 14 slides

Bootstrap Estimation of the Predictive Distributions of Reserves Using Paid and Incurred Claims

331 views • 13 slides

Classical regression review

Classical regression review. Important equations Functional form of the regression Regression coefficients Standard error Coefficient of determination Variance of coefficients Variance of regression Variance of prediction . Practice example. Example problem. % Data

254 views • 12 slides

Summarized by Yung-Kyun Noh Biointelligence Laboratory, Seoul National University

Ch 3. Linear Models for Regression (2/2) Pattern Recognition and Machine Learning, C. M. Bishop, 2006. Summarized by Yung-Kyun Noh Biointelligence Laboratory, Seoul National University http://bi.snu.ac.kr/. Contents. 3.4 Bayesian Model Comparison 3.5 The Evidence Approximation

260 views • 12 slides

Summarized by Yung-Kyun Noh Biointelligence Laboratory, Seoul National University

Ch 3. Linear Models for Regression (2/2) Pattern Recognition and Machine Learning, C. M. Bishop, 2006. Summarized by Yung-Kyun Noh Biointelligence Laboratory, Seoul National University http://bi.snu.ac.kr/. Contents. 3.4 Bayesian Model Comparison 3.5 The Evidence Approximation

125 views • 12 slides

Aquatic Gap Analysis and Biodiversity Management

IRIS. Hydrographic Base Layer. Aquatic GAP uses a modified version of the National Hydrography Dataset (NHD). The original dataset is a product of the collaboration between the USGS and the USEPA.

107 views • 1 slides