Search results for: fundamentals-of-nonparametric-bayesian-inference-cambridge-series-in-statistical-and-probabilistic-mathematics

Fundamentals of Nonparametric Bayesian Inference

Author : Subhashis Ghosal
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Bayesian nonparametrics comes of age with this landmark text synthesizing theory, methodology and computation.

2017 MATRIX Annals

Author : Jan de Gier
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​MATRIX is Australia’s international and residential mathematical research institute. It facilitates new collaborations and mathematical advances through intensive residential research programs, each 1-4 weeks in duration. This book is a scientific record of the eight programs held at MATRIX in its second year, 2017: - Hypergeometric Motives and Calabi–Yau Differential Equations - Computational Inverse Problems - Integrability in Low-Dimensional Quantum Systems - Elliptic Partial Differential Equations of Second Order: Celebrating 40 Years of Gilbarg and Trudinger’s Book - Combinatorics, Statistical Mechanics, and Conformal Field Theory - Mathematics of Risk - Tutte Centenary Retreat - Geometric R-Matrices: from Geometry to Probability The articles are grouped into peer-reviewed contributions and other contributions. The peer-reviewed articles present original results or reviews on a topic related to the MATRIX program; the remaining contributions are predominantly lecture notes or short articles based on talks or activities at MATRIX.

Mathematical Foundations of Infinite Dimensional Statistical Models

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Predictive Statistics

Author : Bertrand S. Clarke
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All scientific disciplines prize predictive success. Conventional statistical analyses, however, treat prediction as secondary, instead focusing on modeling and hence estimation, testing, and detailed physical interpretation, tackling these tasks before the predictive adequacy of a model is established. This book outlines a fully predictive approach to statistical problems based on studying predictors; the approach does not require predictors correspond to a model although this important special case is included in the general approach. Throughout, the point is to examine predictive performance before considering conventional inference. These ideas are traced through five traditional subfields of statistics, helping readers to refocus and adopt a directly predictive outlook. The book also considers prediction via contemporary 'black box' techniques and emerging data types and methodologies where conventional modeling is so difficult that good prediction is the main criterion available for evaluating the performance of a statistical method. Well-documented open-source R code in a Github repository allows readers to replicate examples and apply techniques to other investigations.

Essentials of Statistical Inference

Author : G. A. Young
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Concise account of main approaches; first textbook to synthesize modern computation with basic theory.

Asymptotic Statistics

Author : A. W. van der Vaart
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This book is an introduction to the field of asymptotic statistics. The treatment is both practical and mathematically rigorous. In addition to most of the standard topics of an asymptotics course, including likelihood inference, M-estimation, the theory of asymptotic efficiency, U-statistics, and rank procedures, the book also presents recent research topics such as semiparametric models, the bootstrap, and empirical processes and their applications. The topics are organized from the central idea of approximation by limit experiments, which gives the book one of its unifying themes. This entails mainly the local approximation of the classical i.i.d. set up with smooth parameters by location experiments involving a single, normally distributed observation. Thus, even the standard subjects of asymptotic statistics are presented in a novel way. Suitable as a graduate or Master's level statistics text, this book will also give researchers an overview of research in asymptotic statistics.

Computational Bayesian Statistics

Author : M. Antónia Amaral Turkman
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This integrated introduction to fundamentals, computation, and software is your key to understanding and using advanced Bayesian methods.

Handbook of Parametric and Nonparametric Statistical Procedures

Author : David Sheskin
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The fourth edition of the popular Handbook of Parametric and Nonparametric Statistical Procedures covers 160 of the most commonly used parametric and nonparametric statistical procedures encountered in theory and in practice. Featuring 26 new statistical tests and examples as well as 55 new figures, this edition provides new material on regression diagnostics, including the Durbin-Watson test. The book provides a new section on multivariate analysis with additional information on matrix algebra, uses of Hotellings T2, and multivariate analysis of variance and covariance. Sections on medical statistics, clinical trials, survival analysis, and analysis of censored data are also presented in this edition.

Practical Statistics for Astronomers

Author : J. V. Wall
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Bringing together relevant statistical and probabilistic techniques, a practical manual for advanced undergraduate and graduate students and professional astronomers.

Breakthroughs in Statistics Foundations and basic theory

Author : Samuel Kotz
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This is a two volume collection of seminal papers in the statistical sciences written during the past 100 years. These papers have each had an outstanding influence on the development of statistical theory and practice over the last century. Each paper is preceded by an introduction written by an authority in the field providing background information and assessing its influence. Readers will enjoy a fresh outlook on now well-established features of statistical techniques and philosophy by becoming acquainted with the ways they have been developed. It is hoped that some readers will be stimulated to study some of the references provided in the Introductions (and also in the papers themselves) and so attain a deeper background knowledge of the basis of their work.

Grundbegriffe der Wahrscheinlichkeitsrechnung

Author : Andrei Nikolaevich Kolmogoroff
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Robustness of Bayesian Analyses

Author : Joseph B. Kadane
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Readings in Mathematical Psychology

Author : Robert Duncan Luce
File Size : 66.45 MB
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Bayesian Inference and Decision Techniques

Author : Prem K. Goel
File Size : 65.32 MB
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The primary objective of this volume is to describe the impact of Professor Bruno de Finetti's contributions on statistical theory and practice, and to provide a selection of recent and applied research in Bayesian statistics and econometrics. Included are papers (all previously unpublished) from leading econometricians and statisticians from several countries. Part I of this book relates most directly to de Finetti's interests whilst Part II deals specifically with the implications of the assumption of finitely additive probability. Parts III & IV discuss applications of Bayesian methodology in econometrics and economic forecasting, and Part V examines assessment of prior parameters in specific parametric setting and foundational issues in probability assessment. The following section deals with state of the art for comparing probability functions and gives an assessment of prior distributions and utility functions. In Parts VII & VIII are a collection of papers on Bayesian methodology for general linear models and time series analysis (the most often used tools in economic modelling), and papers relevant to modelling and forecasting. The remaining two Parts examine, respectively, optimality considerations and the effectiveness of the Conditionality-Likelihood Principle as a vehicle to convince the non-Bayesians about the usefulness of the Bayesian paradigm.

Modern Statistical Methods for Astronomy

Author : Eric D. Feigelson
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"Modern astronomical research is beset with a vast range of statistical challenges, ranging from reducing data from megadatasets to characterizing an amazing variety of variable celestial objects or testing astrophysical theory. Yet most astronomers still use a narrow suite of traditional statistical methods. Linking astronomy to the world of modern statistics, this volume is a unique resource, introducing astronomers to advanced statistics through ready-to-use code in the public-domain R statistical software environment"--

Peterson s Guide to Graduate Programs in the Physical Sciences and Mathematics 1991

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Bulletin Institute of Mathematical Statistics

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Bayesian Inference

Author : Nicholas G. Polson
File Size : 85.28 MB
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Journal of the American Statistical Association

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Bayesian Analysis in Econometrics and Statistics

Author : Arnold Zellner
File Size : 33.15 MB
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This book presents some of Arnold Zellner's outstanding contributions to the philosophy, theory and application of Bayesian analysis, particularly as it relates to statistics, econometrics and economics. The volume contains both previously published and new material which cite and discuss the work of Bayesians who have made a contribution by helping researchers and analysts in many professions to become more effective in learning from data and making decisions. Bayesian and non-Bayesian approaches are compared in several papers. Other articles include theoretical and applied results on estimation, model comparison, prediction, forecasting, prior densities, model formulation and hypothesis testing. In addition, a new information processing approach is presented that yields Bayes's Theorem as a perfectly efficient information processing rule.This volume will be essential reading for academics and students interested in qualitative methods as well as industrial analysts and government officials.