Maximum Entropy and Bayesian Methods
|
|
- Dale O’Neal’
- 5 years ago
- Views:
Transcription
1 Maximum Entropy and Bayesian Methods Santa Fe, New Mexico, U.S.A., 1995 Proceedings ofthe Fifieenth International Workshop on Maximum Entropy and Bayesian Methods edited by Kenneth M. Hanson Dynamic Experimentation Division, Los Alamos National Laboratory, Los Alamos, New Mexico, U.S.A. and Richard N. Silver Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico, U.S.A. KLUWER ACADEMIC PUBLISHERS DORDRECHT / BOSTON / LONDON
2 f Preface Participant List WORKSHOP PRESENTATIONS NOT INCLUDED IN THESE PROCEEDINGS xiii xvii *xi RECONSTRUCTION OF THE PROBABILITY DENSITY FUNCTION IMPLICIT IN OPTION PRICES FROMINCOMPLETE AND NOISY DATA R. J. Hawkins, M. Rubinstein and G. J. Daniell 1 MODEL SELECTION AND PARAMETER ESTIMATION FOR EXPONENTIAL SIGNALS A. Ramaswami and G. L. Bretthorst 9 HIERARCHICAL BAYESIAN TIME-SERIES MODELS L. M. Berliner 15 BAYESIAN TIME SERIES: MODELS AND COMPUTATIONS FOR THE ANALYSIS OF TIME SERIES IN THE PHYSICAL SCIENCES M. West 23 MAXENT, MATHEMATICS, AND INFORMATION THEORY I. Csiszar. 35 BAYESIAN ESTIMATION OF THE VON MISES CONCENTRATION PARAMETER D. L. Dowe, J. J. Oliver, R. A. Baxter and C. S. Wallace 51 A CHARACTERIZATION OF THE DIRICHLET DISTRIBUTION WITH APPLICATION TO LEARNING BAYESIAN NETWORKS D. Geiger and D. Heckerman 61 THE BOOTSTRAP IS INCONSISTENT WITH PROBABILITY THEORY D. H. Wolpert 69
3 VI DATA-DRIVEN PRIORS FOR HYPERPARAMETERS IN REGULARIZATION D. Keren and M. Werman 77 MIXTURE MODELING TO INCORPORATE MEANINGFUL CONSTRAINTS INTO LEARNING I. Tchoumatchenko and J.-G. Ganascia 85 MAXIMUM ENTROPY (MAXENT) METHOD IN EXPERT SYSTEMS AND INTELLIGENT CONTROL: NEW POSSIBILITIES AND LIMITATIONS V. Kreinovich, H. T. Nguyen and E. A. Walker 93 THE DE FINETTI TRANSFORM S. J. Press 101 CONTINUUM MODELS FOR BAYESIAN IMAGE MATCHING J. C. Gee and P. D. Peralta 109 MECHANICAL MODELS AS PRIORS IN BAYESIAN TOMOGRAPHIC RECONTRUCTION A. Rangarajan, S.-J. Lee and G. Gindi 117 THE BAYES INFERENCE ENGINE K. M. Hanson and G. S. Cunningham 125 A FÜLL BAYESIAN APPRO ACH FOR INVERSE PROBLEM A. Mohammad-Djafari 135 PIXON-BASED MULTIRESOLUTION IMAGE RECONSTRUCTION AND QUANTIFICATION OF IMAGE INFORMATION CONTENT R. C. Puetter 145 BAYESIAN MULTIMODAL EVIDENCE COMPUTATION BY ADAPTWE TEMPERING MCMC M.-D. Wu and W. J. Fitzgerald 153 BAYESIAN INFERENCE AND THE ANALYTIC CONTINUATION OF IMAGINARY-TIME QUANTUM MONTE CARLO DATA J. E. Gubernatis, J. Bonca and M. Jarrell 163 f
4 vn SPECTRAL PROPERTIES FROM QUANTUM MONTE CARLO DATA: A CONSISTENT APPROACH R. Preuss, W. Von der Linden and W. Hanke 171 AN APPLICATION OF MAXIMUM ENTROPY METHOD TO DYNAMICAL CORRELATION FUNCTIONS AT ZERO TEMPERATURE H. Pang, H. Akhlaghpour and M. Jarrell 179 CHEBYSHEV MOMENT PROBLEMS: MAXIMUM ENTROPY AND KERNEL POLYNOMIAL METHODS R. N. Silver, H. Roeder, A. F. Voter and J. D. Kress,187 CLUSTER EXPANSIONS AND ITERATIVE SCALING FOR MAXIMUM- ENTROPY LANGUAGE MODELS J. D. Lafferty and B. Suhm 195 A MAXENT TOMOGRAPHY METHOD FOR ESTIMATING FISH DENSITIES IN A COMMERCIAL FISHERY S. Lizamore, M. Vignaux and G. A. Vignaux 203 TOWARD OPTIMAL OBSERVER PERFORMANCE OF DETECTION AND DISCRIMINATION TASKS ON RECONSTRUCTIONS FROM SPARSE DATA R. F. Wagner, K. J. Myers, D. G. Brown, M. P. Anderson and K. M. Hanson 211 ENTROPIES FOR DISSIPATIVE FLUIDS AND MAGNETOFLUIDS WITHOUT DISCRETIZATION D. Montgomery 221 ON THE IMPORTANCE OF a MARGINALIZATION IN MAXIMUM ENTROPY R. Fischer, W. Von der Linden and V. Dose 229 QUANTUM MECHANICS AS AN EXOTIC PROBABILITY THEORY S. Youssef 237 BAYESIAN PARAMETER ESTIMATION OF NUCLEAR-FUSION CONFINEMENT TIME SCALING LAWS V. Dose, W. Von der Linden and A. Garrett 245
5 Vlll HIERARCHICAL SEGMENTATION OF RANGE AND COLOR IMAGES BASED ON BAYESIAN DECISION THEORY P. Boulanger 251 PRIORS ON MEASURES J. Skilling and S. Sibisi 261 DETERMINING WHETHER TWO DATA SETS ARE FROM THE SAME DISTRIBUTION D. H. Wolpert 271 OCCAM'S RAZOR FOR PARAMETRIC FAMILIES AND PRIORS ON THE SPACE OF DISTRIBUTIONS V. Balasubramanian 277 SKIN AND MAXIMUM ENTROPY: A HIDDEN COMPLICITY? B. Dubertret, N. Rivier and G. Schliecker 285 PREDICTING THE ACCURACY OF BAYES CLASSIFIERS R. R. Snapp 295 MAXIMUM ENTROPY ANALYSIS OF GENETIC ALGORITHMS J. L. Shapiro, M. Rattray and A. Prügel-Bennett 303 DATA FUSION IN THE FIELD OF NON DESTRUCTIVE TESTING S. Gautier, G. Le Besnerais, A. Mohammad-Djafari and B. Lavayssiere 311 DUAL STATISTICAL MECHANICAL THEORY FOR UNSUPERVISED AND SUPERVISED LEARNING G. Deco and B. Schürmann 317 COMPLEX SINUSOID ANALYSIS BY BAYESIAN DECONVOLUTION OF THE DISCRETE FOURIER TRANSFORM F. Dublanchet, P. Duvaut and J. Idier 323 STATISTICAL MECHANICS OF CHOICE P. S. Faynzilberg 329
6 ix RATIONAL NEURAL MODELS BASED ON INFORMATION THEORY R. L. Fry 335 A NEW ENTROPY MEASURE WITH THE EXPLICIT NOTION OF COMPLEXITY W. Holender 341 MAXIMUM ENTROPY STATES AND COHERENT STRUCTURES IN MAGNETOHYDRODYNAMICS R. Jordan and B. Turkington 347 A LOGNORMAL STATE OF KNOWLEDGE P. R. Dukes and E. G. Larson 355 PIXON-BASED MULTIRESOLUTION IMAGE RECONSTRUCTION FOR YOHKOH'S HARD X-RAY TELESCOPE T. Metcalf, H. S. Hudson, T. Kosugi, R. C. Puetter and R. K. Pifia 361 BAYESIAN METHODS FOR INTERPRETING PLUTONIUM URINALYSIS DATA G. Miller and W. C. Inkret 367 THE INFORMATION CONTENT OF SONAR ECHOES R. Pitre 375 OBJECTIVE PRIOR FOR COSMOLOGICAL PARAMETERS G. Evrard 381 MEAL ESTIMATION: ACCEPTABLE-LIKELIHOOD EXTENSIONS OF MAXENT P. S. Faynzilberg 387 ON CURVE FITTING WITH TWO-DIMENSIONAL UNCERTAINTIES F. H. Fröhner 393 BAYESIAN INFERENCE IN SEARCH FOR THE IN VIVO T 2 DECAY-RATE DISTRIBUTION IN HUMAN BRAIN I. Gideoni 407
7 X BAYESIAN COMPARISON OF FIT PARAMETERS: APPLICATION TO TIME-RESOLVED X-RAY SPECTROSCOPY V. Kashyap 413 EDGE ENTROPY AND VISUAL COMPLEXITY P. Moos and J. P. Lewis 419 MAXIMUM ENTROPY TOMOGRAPHY C. T. Mottershead 425 BAYESIAN REGULARIZATION OF SOME SEISMIC OPERATORS M. D. Sacchi and T. J. Ulrych 431 MULTIMODALITY BAYESIAN ALGORITHM FOR IMAGE RECONSTRUCTION IN POSITRON EMISSION TOMOGRAPHY S. Sastry, J.W. Vanmeter and R.E. Carson 437 EVIDENCE INTEGRALS W. Von der Linden, R. Fischer and V. Dose 443 Index 449
Pattern Recognition and Machine Learning
Christopher M. Bishop Pattern Recognition and Machine Learning ÖSpri inger Contents Preface Mathematical notation Contents vii xi xiii 1 Introduction 1 1.1 Example: Polynomial Curve Fitting 4 1.2 Probability
More informationPATTERN CLASSIFICATION
PATTERN CLASSIFICATION Second Edition Richard O. Duda Peter E. Hart David G. Stork A Wiley-lnterscience Publication JOHN WILEY & SONS, INC. New York Chichester Weinheim Brisbane Singapore Toronto CONTENTS
More informationProbing the covariance matrix
Probing the covariance matrix Kenneth M. Hanson Los Alamos National Laboratory (ret.) BIE Users Group Meeting, September 24, 2013 This presentation available at http://kmh-lanl.hansonhub.com/ LA-UR-06-5241
More informationUnfolding techniques for neutron spectrometry
Uncertainty Assessment in Computational Dosimetry: A comparison of Approaches Unfolding techniques for neutron spectrometry Physikalisch-Technische Bundesanstalt Braunschweig, Germany Contents of the talk
More informationProbing the covariance matrix
Probing the covariance matrix Kenneth M. Hanson T-6, Nuclear Physics, Los Alamos National Laboratory, Los Alamos, New Mexico, USA 8755 kmh@lanl.gov Abstract. By drawing an analogy between the logarithm
More informationThe MaxEnt Method Applied to Spectral Analysis and Image Reconstruction
The MaxEnt Method Applied to Spectral Analysis and Image Reconstruction Suzette C. Lizamore Institute of Statistics and Operations Research Victoria University of Wellington suzette@isor.vuw.ac.nz Abstract
More informationNonparametric Bayesian Methods (Gaussian Processes)
[70240413 Statistical Machine Learning, Spring, 2015] Nonparametric Bayesian Methods (Gaussian Processes) Jun Zhu dcszj@mail.tsinghua.edu.cn http://bigml.cs.tsinghua.edu.cn/~jun State Key Lab of Intelligent
More informationMATHEMATICS OF DATA FUSION
MATHEMATICS OF DATA FUSION by I. R. GOODMAN NCCOSC RDTE DTV, San Diego, California, U.S.A. RONALD P. S. MAHLER Lockheed Martin Tactical Defences Systems, Saint Paul, Minnesota, U.S.A. and HUNG T. NGUYEN
More informationSubjective and Objective Bayesian Statistics
Subjective and Objective Bayesian Statistics Principles, Models, and Applications Second Edition S. JAMES PRESS with contributions by SIDDHARTHA CHIB MERLISE CLYDE GEORGE WOODWORTH ALAN ZASLAVSKY \WILEY-
More informationPART I INTRODUCTION The meaning of probability Basic definitions for frequentist statistics and Bayesian inference Bayesian inference Combinatorics
Table of Preface page xi PART I INTRODUCTION 1 1 The meaning of probability 3 1.1 Classical definition of probability 3 1.2 Statistical definition of probability 9 1.3 Bayesian understanding of probability
More informationA generalized information theoretical approach to tomographic reconstruction
INSTITUTE OF PHYSICS PUBLISHING JOURNAL OF PHYSICS A: MATHEMATICAL AND GENERAL J. Phys. A: Math. Gen. 34 (2001) 1271 1283 www.iop.org/journals/ja PII: S0305-4470(01)19639-3 A generalized information theoretical
More informationAdvanced Machine Learning
Advanced Machine Learning Nonparametric Bayesian Models --Learning/Reasoning in Open Possible Worlds Eric Xing Lecture 7, August 4, 2009 Reading: Eric Xing Eric Xing @ CMU, 2006-2009 Clustering Eric Xing
More informationStatistical Rock Physics
Statistical - Introduction Book review 3.1-3.3 Min Sun March. 13, 2009 Outline. What is Statistical. Why we need Statistical. How Statistical works Statistical Rock physics Information theory Statistics
More informationInformation Dynamics Foundations and Applications
Gustavo Deco Bernd Schürmann Information Dynamics Foundations and Applications With 89 Illustrations Springer PREFACE vii CHAPTER 1 Introduction 1 CHAPTER 2 Dynamical Systems: An Overview 7 2.1 Deterministic
More informationMachine Learning Overview
Machine Learning Overview Sargur N. Srihari University at Buffalo, State University of New York USA 1 Outline 1. What is Machine Learning (ML)? 2. Types of Information Processing Problems Solved 1. Regression
More informationRonald Christensen. University of New Mexico. Albuquerque, New Mexico. Wesley Johnson. University of California, Irvine. Irvine, California
Texts in Statistical Science Bayesian Ideas and Data Analysis An Introduction for Scientists and Statisticians Ronald Christensen University of New Mexico Albuquerque, New Mexico Wesley Johnson University
More informationIntroduction to the Mathematics of Medical Imaging
Introduction to the Mathematics of Medical Imaging Second Edition Charles L. Epstein University of Pennsylvania Philadelphia, Pennsylvania EiaJTL Society for Industrial and Applied Mathematics Philadelphia
More informationSpatial Bayesian Nonparametrics for Natural Image Segmentation
Spatial Bayesian Nonparametrics for Natural Image Segmentation Erik Sudderth Brown University Joint work with Michael Jordan University of California Soumya Ghosh Brown University Parsing Visual Scenes
More informationCarlos C. Rodríguez CURRICULUM VITAE
Carlos C. Rodríguez 1 Birth 6/19/56 Marital Status Married, two sons. Address CURRICULUM VITAE Carlos C. Rodríguez Office: Department of Mathematics and Statistics. The University at Albany. SUNY 1400
More informationBAYESIAN PROBABILITY THEORY
BAYESIAN PROBABILITY THEORY From the basics to the forefront of modern research, this book presents all aspects of probability theory, statistics and data analysis from a Bayesian perspective for physicists
More informationOverview of Statistical Tools. Statistical Inference. Bayesian Framework. Modeling. Very simple case. Things are usually more complicated
Fall 3 Computer Vision Overview of Statistical Tools Statistical Inference Haibin Ling Observation inference Decision Prior knowledge http://www.dabi.temple.edu/~hbling/teaching/3f_5543/index.html Bayesian
More informationLinear Algebra and Probability
Linear Algebra and Probability for Computer Science Applications Ernest Davis CRC Press Taylor!* Francis Group Boca Raton London New York CRC Press is an imprint of the Taylor Sc Francis Croup, an informa
More informationShould all Machine Learning be Bayesian? Should all Bayesian models be non-parametric?
Should all Machine Learning be Bayesian? Should all Bayesian models be non-parametric? Zoubin Ghahramani Department of Engineering University of Cambridge, UK zoubin@eng.cam.ac.uk http://learning.eng.cam.ac.uk/zoubin/
More informationBayesian room-acoustic modal analysis
Bayesian room-acoustic modal analysis Wesley Henderson a) Jonathan Botts b) Ning Xiang c) Graduate Program in Architectural Acoustics, School of Architecture, Rensselaer Polytechnic Institute, Troy, New
More informationIrr. Statistical Methods in Experimental Physics. 2nd Edition. Frederick James. World Scientific. CERN, Switzerland
Frederick James CERN, Switzerland Statistical Methods in Experimental Physics 2nd Edition r i Irr 1- r ri Ibn World Scientific NEW JERSEY LONDON SINGAPORE BEIJING SHANGHAI HONG KONG TAIPEI CHENNAI CONTENTS
More informationUse of probability gradients in hybrid MCMC and a new convergence test
Use of probability gradients in hybrid MCMC and a new convergence test Ken Hanson Methods for Advanced Scientific Simulations Group This presentation available under http://www.lanl.gov/home/kmh/ June
More informationSTA414/2104. Lecture 11: Gaussian Processes. Department of Statistics
STA414/2104 Lecture 11: Gaussian Processes Department of Statistics www.utstat.utoronto.ca Delivered by Mark Ebden with thanks to Russ Salakhutdinov Outline Gaussian Processes Exam review Course evaluations
More information9/12/17. Types of learning. Modeling data. Supervised learning: Classification. Supervised learning: Regression. Unsupervised learning: Clustering
Types of learning Modeling data Supervised: we know input and targets Goal is to learn a model that, given input data, accurately predicts target data Unsupervised: we know the input only and want to make
More informationRecent Advances in Bayesian Inference Techniques
Recent Advances in Bayesian Inference Techniques Christopher M. Bishop Microsoft Research, Cambridge, U.K. research.microsoft.com/~cmbishop SIAM Conference on Data Mining, April 2004 Abstract Bayesian
More informationUnsupervised Learning
Unsupervised Learning Bayesian Model Comparison Zoubin Ghahramani zoubin@gatsby.ucl.ac.uk Gatsby Computational Neuroscience Unit, and MSc in Intelligent Systems, Dept Computer Science University College
More informationLos Alamos, New Mexico 87545
Los A l a m National Laboratory is operated by the University of California for the United States Department of Energy under contract W-7405-ENG-36. TITLE: BAYESIAN INFERENCE AND THE ANALYTIC CONTINUATION
More informationKarl-Rudolf Koch Introduction to Bayesian Statistics Second Edition
Karl-Rudolf Koch Introduction to Bayesian Statistics Second Edition Karl-Rudolf Koch Introduction to Bayesian Statistics Second, updated and enlarged Edition With 17 Figures Professor Dr.-Ing., Dr.-Ing.
More informationGaussian Models
Gaussian Models ddebarr@uw.edu 2016-04-28 Agenda Introduction Gaussian Discriminant Analysis Inference Linear Gaussian Systems The Wishart Distribution Inferring Parameters Introduction Gaussian Density
More informationContents. Part I: Fundamentals of Bayesian Inference 1
Contents Preface xiii Part I: Fundamentals of Bayesian Inference 1 1 Probability and inference 3 1.1 The three steps of Bayesian data analysis 3 1.2 General notation for statistical inference 4 1.3 Bayesian
More informationEE562 ARTIFICIAL INTELLIGENCE FOR ENGINEERS
EE562 ARTIFICIAL INTELLIGENCE FOR ENGINEERS Lecture 16, 6/1/2005 University of Washington, Department of Electrical Engineering Spring 2005 Instructor: Professor Jeff A. Bilmes Uncertainty & Bayesian Networks
More information1 Introduction Overview of the Book How to Use this Book Introduction to R 10
List of Tables List of Figures Preface xiii xv xvii 1 Introduction 1 1.1 Overview of the Book 3 1.2 How to Use this Book 7 1.3 Introduction to R 10 1.3.1 Arithmetic Operations 10 1.3.2 Objects 12 1.3.3
More informationPrerequisite: STATS 7 or STATS 8 or AP90 or (STATS 120A and STATS 120B and STATS 120C). AP90 with a minimum score of 3
University of California, Irvine 2017-2018 1 Statistics (STATS) Courses STATS 5. Seminar in Data Science. 1 Unit. An introduction to the field of Data Science; intended for entering freshman and transfers.
More informationNeutron inverse kinetics via Gaussian Processes
Neutron inverse kinetics via Gaussian Processes P. Picca Politecnico di Torino, Torino, Italy R. Furfaro University of Arizona, Tucson, Arizona Outline Introduction Review of inverse kinetics techniques
More informationGeophysical Data Analysis: Discrete Inverse Theory
Geophysical Data Analysis: Discrete Inverse Theory MATLAB Edition William Menke Lamont-Doherty Earth Observatory and Department of Earth and Environmental Sciences Columbia University. ' - Palisades, New
More informationCourse in Data Science
Course in Data Science About the Course: In this course you will get an introduction to the main tools and ideas which are required for Data Scientist/Business Analyst/Data Analyst. The course gives an
More informationBeyond Uniform Priors in Bayesian Network Structure Learning
Beyond Uniform Priors in Bayesian Network Structure Learning (for Discrete Bayesian Networks) scutari@stats.ox.ac.uk Department of Statistics April 5, 2017 Bayesian Network Structure Learning Learning
More informationLearning From Data Lecture 15 Reflecting on Our Path - Epilogue to Part I
Learning From Data Lecture 15 Reflecting on Our Path - Epilogue to Part I What We Did The Machine Learning Zoo Moving Forward M Magdon-Ismail CSCI 4100/6100 recap: Three Learning Principles Scientist 2
More informationDeep learning / Ian Goodfellow, Yoshua Bengio and Aaron Courville. - Cambridge, MA ; London, Spis treści
Deep learning / Ian Goodfellow, Yoshua Bengio and Aaron Courville. - Cambridge, MA ; London, 2017 Spis treści Website Acknowledgments Notation xiii xv xix 1 Introduction 1 1.1 Who Should Read This Book?
More informationSTA414/2104 Statistical Methods for Machine Learning II
STA414/2104 Statistical Methods for Machine Learning II Murat A. Erdogdu & David Duvenaud Department of Computer Science Department of Statistical Sciences Lecture 3 Slide credits: Russ Salakhutdinov Announcements
More informationWhy Try Bayesian Methods? (Lecture 5)
Why Try Bayesian Methods? (Lecture 5) Tom Loredo Dept. of Astronomy, Cornell University http://www.astro.cornell.edu/staff/loredo/bayes/ p.1/28 Today s Lecture Problems you avoid Ambiguity in what is random
More informationLectures in AstroStatistics: Topics in Machine Learning for Astronomers
Lectures in AstroStatistics: Topics in Machine Learning for Astronomers Jessi Cisewski Yale University American Astronomical Society Meeting Wednesday, January 6, 2016 1 Statistical Learning - learning
More informationThe Bayesian Choice. Christian P. Robert. From Decision-Theoretic Foundations to Computational Implementation. Second Edition.
Christian P. Robert The Bayesian Choice From Decision-Theoretic Foundations to Computational Implementation Second Edition With 23 Illustrations ^Springer" Contents Preface to the Second Edition Preface
More informationMultilevel Statistical Models: 3 rd edition, 2003 Contents
Multilevel Statistical Models: 3 rd edition, 2003 Contents Preface Acknowledgements Notation Two and three level models. A general classification notation and diagram Glossary Chapter 1 An introduction
More informationProbability Theory and Machine Learning in Science
Probability Theory and Machine Learning in Science Ji-Hun Kim Seoul National University ROSAEC Workshop 2013 Ji-Hun Kim (SNU) Probability Theory and ML in Science ROSAEC Workshop 2013 1 / 26 Outline 1
More informationInternational Journal "Information Theories & Applications" Vol.14 /
International Journal "Information Theories & Applications" Vol.4 / 2007 87 or 2) Nˆ t N. That criterion and parameters F, M, N assign method of constructing sample decision function. In order to estimate
More informationVariational Methods in Bayesian Deconvolution
PHYSTAT, SLAC, Stanford, California, September 8-, Variational Methods in Bayesian Deconvolution K. Zarb Adami Cavendish Laboratory, University of Cambridge, UK This paper gives an introduction to the
More informationThe Maximum Entropy Method
Nailong Wu The Maximum Entropy Method With 53 Figures Springer 1. Introduction 1 1.1 What is the Maximum Entropy Method 1 1.2 Definition of Entropy 4 1.3 Rationale of the Maximum Entropy Method 6 1.4 Present
More informationCOPYRIGHTED MATERIAL CONTENTS. Preface Preface to the First Edition
Preface Preface to the First Edition xi xiii 1 Basic Probability Theory 1 1.1 Introduction 1 1.2 Sample Spaces and Events 3 1.3 The Axioms of Probability 7 1.4 Finite Sample Spaces and Combinatorics 15
More information6.036 midterm review. Wednesday, March 18, 15
6.036 midterm review 1 Topics covered supervised learning labels available unsupervised learning no labels available semi-supervised learning some labels available - what algorithms have you learned that
More informationMachine Learning A Bayesian and Optimization Perspective
Machine Learning A Bayesian and Optimization Perspective Sergios Theodoridis AMSTERDAM BOSTON HEIDELBERG LONDON NEW YORK OXFORD PARIS SAN DIEGO SAN FRANCISCO SINGAPORE SYDNEY TOKYO Academic Press is an
More informationNumerical Analysis for Statisticians
Kenneth Lange Numerical Analysis for Statisticians Springer Contents Preface v 1 Recurrence Relations 1 1.1 Introduction 1 1.2 Binomial CoefRcients 1 1.3 Number of Partitions of a Set 2 1.4 Horner's Method
More informationBayesian X-ray Computed Tomography using a Three-level Hierarchical Prior Model
L. Wang, A. Mohammad-Djafari, N. Gac, MaxEnt 16, Ghent, Belgium. 1/26 Bayesian X-ray Computed Tomography using a Three-level Hierarchical Prior Model Li Wang, Ali Mohammad-Djafari, Nicolas Gac Laboratoire
More informationPATTERN RECOGNITION AND MACHINE LEARNING
PATTERN RECOGNITION AND MACHINE LEARNING Chapter 1. Introduction Shuai Huang April 21, 2014 Outline 1 What is Machine Learning? 2 Curve Fitting 3 Probability Theory 4 Model Selection 5 The curse of dimensionality
More informationParameter Estimation. William H. Jefferys University of Texas at Austin Parameter Estimation 7/26/05 1
Parameter Estimation William H. Jefferys University of Texas at Austin bill@bayesrules.net Parameter Estimation 7/26/05 1 Elements of Inference Inference problems contain two indispensable elements: Data
More informationLatent Dirichlet Allocation (LDA)
Latent Dirichlet Allocation (LDA) A review of topic modeling and customer interactions application 3/11/2015 1 Agenda Agenda Items 1 What is topic modeling? Intro Text Mining & Pre-Processing Natural Language
More informationPATTERN RECOGNITION AND MACHINE LEARNING CHAPTER 2: PROBABILITY DISTRIBUTIONS
PATTERN RECOGNITION AND MACHINE LEARNING CHAPTER 2: PROBABILITY DISTRIBUTIONS Parametric Distributions Basic building blocks: Need to determine given Representation: or? Recall Curve Fitting Binary Variables
More informationStatistical and Inductive Inference by Minimum Message Length
C.S. Wallace Statistical and Inductive Inference by Minimum Message Length With 22 Figures Springer Contents Preface 1. Inductive Inference 1 1.1 Introduction 1 1.2 Inductive Inference 5 1.3 The Demise
More informationComputer Vision Group Prof. Daniel Cremers. 2. Regression (cont.)
Prof. Daniel Cremers 2. Regression (cont.) Regression with MLE (Rep.) Assume that y is affected by Gaussian noise : t = f(x, w)+ where Thus, we have p(t x, w, )=N (t; f(x, w), 2 ) 2 Maximum A-Posteriori
More informationHyperparameter estimation in Dirichlet process mixture models
Hyperparameter estimation in Dirichlet process mixture models By MIKE WEST Institute of Statistics and Decision Sciences Duke University, Durham NC 27706, USA. SUMMARY In Bayesian density estimation and
More informationOBJECT DETECTION AND RECOGNITION IN DIGITAL IMAGES
OBJECT DETECTION AND RECOGNITION IN DIGITAL IMAGES THEORY AND PRACTICE Bogustaw Cyganek AGH University of Science and Technology, Poland WILEY A John Wiley &. Sons, Ltd., Publication Contents Preface Acknowledgements
More informationAn Empirical-Bayes Score for Discrete Bayesian Networks
An Empirical-Bayes Score for Discrete Bayesian Networks scutari@stats.ox.ac.uk Department of Statistics September 8, 2016 Bayesian Network Structure Learning Learning a BN B = (G, Θ) from a data set D
More informationDensity Propagation for Continuous Temporal Chains Generative and Discriminative Models
$ Technical Report, University of Toronto, CSRG-501, October 2004 Density Propagation for Continuous Temporal Chains Generative and Discriminative Models Cristian Sminchisescu and Allan Jepson Department
More informationProbabilistic Graphical Models
Probabilistic Graphical Models Introduction. Basic Probability and Bayes Volkan Cevher, Matthias Seeger Ecole Polytechnique Fédérale de Lausanne 26/9/2011 (EPFL) Graphical Models 26/9/2011 1 / 28 Outline
More informationBayesian Hidden Markov Models and Extensions
Bayesian Hidden Markov Models and Extensions Zoubin Ghahramani Department of Engineering University of Cambridge joint work with Matt Beal, Jurgen van Gael, Yunus Saatci, Tom Stepleton, Yee Whye Teh Modeling
More informationBagging During Markov Chain Monte Carlo for Smoother Predictions
Bagging During Markov Chain Monte Carlo for Smoother Predictions Herbert K. H. Lee University of California, Santa Cruz Abstract: Making good predictions from noisy data is a challenging problem. Methods
More informationSession 5B: A worked example EGARCH model
Session 5B: A worked example EGARCH model John Geweke Bayesian Econometrics and its Applications August 7, worked example EGARCH model August 7, / 6 EGARCH Exponential generalized autoregressive conditional
More informationMore on HMMs and other sequence models. Intro to NLP - ETHZ - 18/03/2013
More on HMMs and other sequence models Intro to NLP - ETHZ - 18/03/2013 Summary Parts of speech tagging HMMs: Unsupervised parameter estimation Forward Backward algorithm Bayesian variants Discriminative
More informationBayesian Nonparametrics for Speech and Signal Processing
Bayesian Nonparametrics for Speech and Signal Processing Michael I. Jordan University of California, Berkeley June 28, 2011 Acknowledgments: Emily Fox, Erik Sudderth, Yee Whye Teh, and Romain Thibaux Computer
More informationWalsh Series and Transforms
Walsh Series and Transforms Theory and Applications by B. Golubov Moscow Institute of Engineering, A. Efimov Moscow Institute of Engineering, and V. Skvortsov Moscow State University, W KLUWER ACADEMIC
More informationBayesian Networks Inference with Probabilistic Graphical Models
4190.408 2016-Spring Bayesian Networks Inference with Probabilistic Graphical Models Byoung-Tak Zhang intelligence Lab Seoul National University 4190.408 Artificial (2016-Spring) 1 Machine Learning? Learning
More information12 slots, 2 hours each. A homework: visualization, simple testing, and simple classification algorithms.
12 slots, 2 hours each. A homework: visualization, simple testing, and simple classification algorithms. Approximate Syllabus: Organization and structure. Intro to R. Set operations. Venn diagramms. De
More informationContents. Preface to the Third Edition (2007) Preface to the Second Edition (1992) Preface to the First Edition (1985) License and Legal Information
Contents Preface to the Third Edition (2007) Preface to the Second Edition (1992) Preface to the First Edition (1985) License and Legal Information xi xiv xvii xix 1 Preliminaries 1 1.0 Introduction.............................
More informationIndependent Component Analysis. Contents
Contents Preface xvii 1 Introduction 1 1.1 Linear representation of multivariate data 1 1.1.1 The general statistical setting 1 1.1.2 Dimension reduction methods 2 1.1.3 Independence as a guiding principle
More informationExploring Monte Carlo Methods
Exploring Monte Carlo Methods William L Dunn J. Kenneth Shultis AMSTERDAM BOSTON HEIDELBERG LONDON NEW YORK OXFORD PARIS SAN DIEGO SAN FRANCISCO SINGAPORE SYDNEY TOKYO ELSEVIER Academic Press Is an imprint
More informationSTA 4273H: Statistical Machine Learning
STA 4273H: Statistical Machine Learning Russ Salakhutdinov Department of Statistics! rsalakhu@utstat.toronto.edu! http://www.utstat.utoronto.ca/~rsalakhu/ Sidney Smith Hall, Room 6002 Lecture 7 Approximate
More informationBayesian time series classification
Bayesian time series classification Peter Sykacek Department of Engineering Science University of Oxford Oxford, OX 3PJ, UK psyk@robots.ox.ac.uk Stephen Roberts Department of Engineering Science University
More informationGraphical Models for Query-driven Analysis of Multimodal Data
Graphical Models for Query-driven Analysis of Multimodal Data John Fisher Sensing, Learning, & Inference Group Computer Science & Artificial Intelligence Laboratory Massachusetts Institute of Technology
More informationStatistical Multisource-Multitarget Information Fusion
Statistical Multisource-Multitarget Information Fusion Ronald P. S. Mahler ARTECH H O U S E BOSTON LONDON artechhouse.com Contents Preface Acknowledgments xxm xxv Chapter 1 Introduction to the Book 1 1.1
More informationSTA 4273H: Statistical Machine Learning
STA 4273H: Statistical Machine Learning Russ Salakhutdinov Department of Statistics! rsalakhu@utstat.toronto.edu! http://www.utstat.utoronto.ca/~rsalakhu/ Sidney Smith Hall, Room 6002 Lecture 3 Linear
More informationStatistical Approaches to Learning and Discovery
Statistical Approaches to Learning and Discovery Bayesian Model Selection Zoubin Ghahramani & Teddy Seidenfeld zoubin@cs.cmu.edu & teddy@stat.cmu.edu CALD / CS / Statistics / Philosophy Carnegie Mellon
More informationU-Likelihood and U-Updating Algorithms: Statistical Inference in Latent Variable Models
U-Likelihood and U-Updating Algorithms: Statistical Inference in Latent Variable Models Jaemo Sung 1, Sung-Yang Bang 1, Seungjin Choi 1, and Zoubin Ghahramani 2 1 Department of Computer Science, POSTECH,
More informationCurve Fitting Re-visited, Bishop1.2.5
Curve Fitting Re-visited, Bishop1.2.5 Maximum Likelihood Bishop 1.2.5 Model Likelihood differentiation p(t x, w, β) = Maximum Likelihood N N ( t n y(x n, w), β 1). (1.61) n=1 As we did in the case of the
More informationAn Introduction to Computer Simulation Methods
An Introduction to Computer Simulation Methods Applications to Physical Systems Second Edition Harvey Gould Department of Physics Clark University Jan Tobochnik Department of Physics Kalamazoo College
More informationIntelligent Systems I
Intelligent Systems I 00 INTRODUCTION Stefan Harmeling & Philipp Hennig 24. October 2013 Max Planck Institute for Intelligent Systems Dptmt. of Empirical Inference Which Card? Opening Experiment Which
More informationMinimum Message Length Inference and Mixture Modelling of Inverse Gaussian Distributions
Minimum Message Length Inference and Mixture Modelling of Inverse Gaussian Distributions Daniel F. Schmidt Enes Makalic Centre for Molecular, Environmental, Genetic & Analytic (MEGA) Epidemiology School
More informationIntroduction to Integrated Data Analysis
7th Workshop on Fusion Data Processing, Validation and Analysis Introduction to Integrated Data Analysis R. Fischer Max-Planck-Institut für Plasmaphysik, Garching EURATOM Association Frascati, Mar 26-28,
More informationMachine Learning! in just a few minutes. Jan Peters Gerhard Neumann
Machine Learning! in just a few minutes Jan Peters Gerhard Neumann 1 Purpose of this Lecture Foundations of machine learning tools for robotics We focus on regression methods and general principles Often
More informationMAD-Bayes: MAP-based Asymptotic Derivations from Bayes
MAD-Bayes: MAP-based Asymptotic Derivations from Bayes Tamara Broderick Brian Kulis Michael I. Jordan Cat Clusters Mouse clusters Dog 1 Cat Clusters Dog Mouse Lizard Sheep Picture 1 Picture 2 Picture 3
More informationP3TMA Experimental Projects
P3TMA Experimental Projects 3 credits Take place @ S1 (from end of September to December); Enters in the average of the second semester. Projects currently available : Stern-Gerlach Experiment Quantum
More informationIntroduction to Machine Learning
Introduction to Machine Learning Brown University CSCI 1950-F, Spring 2012 Prof. Erik Sudderth Lecture 25: Markov Chain Monte Carlo (MCMC) Course Review and Advanced Topics Many figures courtesy Kevin
More informationSTAT 499/962 Topics in Statistics Bayesian Inference and Decision Theory Jan 2018, Handout 01
STAT 499/962 Topics in Statistics Bayesian Inference and Decision Theory Jan 2018, Handout 01 Nasser Sadeghkhani a.sadeghkhani@queensu.ca There are two main schools to statistical inference: 1-frequentist
More informationSTA 4273H: Sta-s-cal Machine Learning
STA 4273H: Sta-s-cal Machine Learning Russ Salakhutdinov Department of Computer Science! Department of Statistical Sciences! rsalakhu@cs.toronto.edu! h0p://www.cs.utoronto.ca/~rsalakhu/ Lecture 2 In our
More informationUncertainty Quantification for Machine Learning and Statistical Models
Uncertainty Quantification for Machine Learning and Statistical Models David J. Stracuzzi Joint work with: Max Chen, Michael Darling, Stephen Dauphin, Matt Peterson, and Chris Young Sandia National Laboratories
More informationNonparmeteric Bayes & Gaussian Processes. Baback Moghaddam Machine Learning Group
Nonparmeteric Bayes & Gaussian Processes Baback Moghaddam baback@jpl.nasa.gov Machine Learning Group Outline Bayesian Inference Hierarchical Models Model Selection Parametric vs. Nonparametric Gaussian
More informationIntroduction to Probabilistic Machine Learning
Introduction to Probabilistic Machine Learning Piyush Rai Dept. of CSE, IIT Kanpur (Mini-course 1) Nov 03, 2015 Piyush Rai (IIT Kanpur) Introduction to Probabilistic Machine Learning 1 Machine Learning
More information