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A History of Parametric Statistical Inference from Bernoulli to Fisher, 1713-1935

This book offers a detailed history of parametric statistical inference. Covering the period between James Bernoulli and R.A. Fisher, it;

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Predictive Inference

of approaches to prediction including those based on parametric, nonparametric, and nonstochastic statistical models, it is devoted mainly to predictive;

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Predictive Inference

of approaches to prediction including those based on parametric, nonparametric, and nonstochastic statistical models, it is devoted mainly to predictive;

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Modes Of Parametric Statistical Inference

highlights from the history of statistical inference. Readers are given historical examples of statistical reasoning used to address practical;

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Statistical Inference for Fractional Diffusion Processes

economics. In model building, statistical inference for stochastic processes is of great importance from both a theoretical and an applications;

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Constrained Statistical Inference

An up-to-date approach to understanding statistical inference Statistical inference is finding useful applications in numerous fields, from;

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Introduction to Probability Theory and Statistical Inference

Discusses probability theory and to many methods used in problems of statistical inference. The Third Edition features material on;

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Statistical Paradigms

This volume consists of a collection of research articles on classical and emerging Statistical Paradigms parametric, non-parametric and;

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Wahrscheinlichkeitsrechnung (Ars Conjectandi) Von Jakob Bernoulli (1713) Uebers. Und Hrsg. Von R. Haussner.

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Wahrscheinlichkeitsrechnung, Ars conjectandi, 1713

Wahrscheinlichkeitsrechnung (Ars Conjectandi) is een boek van Jakob Bernoulli;

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Non-Standard Parametric Statistical Inference

This book discusses the fitting of parametric statistical models to data samples. Emphasis is placed on: (i) how to recognize situations;

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Statistical Inference

Adopting a broad view of statistical inference, this text concentrates on what various techniques do, with mathematical proofs kept to a;

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Advances In Statistical Modeling And Inference

methodology. Increased computing power has spawned entirely new areas of research in computationally-intensive methods, allowing us to move away from;

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Examples in Parametric Inference with R

in the book are fundamental and common to many fields of statistical inference and thus serve as a point of departure for in-depth study. The;

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Machine Interval Experiments

extended experiment allows a statistical decision made with the aid of a computer to be equivalent to a numerical proof. From the integrative;

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Likelihood

, but that of likelihood, the concept introduced by Fisher as a measure of relative support among different hypotheses. Starting from the;

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Statistical Inference

Filling a gap in current Bayesian theory, Statistical Inference: An Integrated Bayesian/Likelihood Approach presents a unified Bayesian;

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Statistical Inference

Filling a gap in current Bayesian theory, Statistical Inference: An Integrated Bayesian/Likelihood Approach presents a unified Bayesian;

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Phylogenetic Inference, Selection Theory, and History of Science

A. W. F. Edwards is one of the most influential mathematical geneticists in the history of the discipline. One of the last students of R. A;

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BAYESIAN INFERENCE

. Topics covered include the central field of statistical inference as well as applications to areas of probability theory, information theory;

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Theory of Statistical Inference

alternatives to parametric methods. Each chapter ends with a set of theoretical and applied exercises integrated with the main text. Problems involving;

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Mathematical Statistics

Presents a unified approach to parametric estimation, confidence intervals, hypothesis testing, and statistical modeling, which are;

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Statistical Modelling and Analysis Techniques

Ronald A. Fisher containing details on his life and contribution to statistical research is also included to motivate young researchers. The;

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A Treatise On Probability - Unabridged

Application Of Probability To Conduct - The Nature Of Statistical Inference - The Law Of Great Numbers - The Theorems Of Bernoulli, Poisson, And;

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Modern Statistical Methods for HCI

design, event history analysis, non-parametric testing and Bayesian inference; the research contained in this book discusses how to communicate;

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Maximum Entropy and Bayesian Methods

Mexico, USA, from July 31 to August 4, 1995. Maximum entropy and Bayesian methods are widely applied to statistical data analysis and scientific;

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Classic Topics on the History of Modern Mathematical Statistics

History of Modern Mathematical Statistics: From Laplace to More Recent Times presents a comprehensive guide to the history of mathematical;

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