Tools For Statistical Inference Book

2 Ratings

Tools For Statistical Inference, , Tools For Statistical Inference

3.5 out of 5 stars based on 2 reviews

Tools For Statistical Inference
Written by author Martin A. Tanner
Published by Springer-Verlag New York, LLC, June 1996
This book provides a unified introduction to a variety of computational algorithms for likelihood and Bayesian inference. The third edition expands the discussion of many of the techniques discussed, includes additional examples, and adds exercise sets at

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Preface
1 Introduction 1
2 Normal Approximations to Likelihoods and to Posteriors 14
3 Nonnormal Approximations to Likelihoods and Posteriors 40
4 The EM Algorithm 64
5 The Data Augmentation Algorithm 90
6 Markov Chain Monte Carlo: The Gibbs Sampler and the Metropolis Algorithm 137
References 193
Subject Index 203

Title: Tools For Statistical Inference

Manufacturer:

Steidl Publishing

Item Number: 9780387946887

Publication Date: June 1996

Number: 3

Product Description: Tools For Statistical Inference

Universal Product Code (UPC): 9780387946887

WonderClub Stock Keeping Unit (WSKU): 9780387946887

Rating: 3.5/5 based on 2 Reviews

Image Location: https://wonderclub.com/images/covers/68/87/9780387946887.jpg

Category: Media >> Books

Weight: 0.200 kg (0.44 lbs)

Width: 0.000 cm (0.00 inches)

Heigh : 0.000 cm (0.00 inches)

Depth: 0.000 cm (0.00 inches)

Date Added: August 25, 2020, Added By: Ross

Date Last Edited: August 25, 2020, Edited By: Ross

Price	Condition	Delivery	Seller	Action
$119.00	Digital	Arrives between May 12 - Jun 11 Login to your account for a direct download. Some files are emailed from Dropbox. Continent $0.00 - World $0.00	WonderClub (9296 total ratings)

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Tony Pope

reviewed Tools For Statistical Inference on September 06, 2015

Tools For Statistical Inference

I really like it because it gives clear intuition to the concepts. Great book for an introduction although the formal mathematics definitions are not maintained.

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	Preface
1	Introduction	1
2	Normal Approximations to Likelihoods and to Posteriors	14
3	Nonnormal Approximations to Likelihoods and Posteriors	40
4	The EM Algorithm	64
5	The Data Augmentation Algorithm	90
6	Markov Chain Monte Carlo: The Gibbs Sampler and the Metropolis Algorithm	137
	References	193
	Subject Index	203