Research and Markets: Data Analysis in Forensic Science: A Bayesian Decision Perspective Explores the Bayesian Decision-Theoretic Perspective Further

DUBLIN--(BUSINESS WIRE)--Research and Markets (http://www.researchandmarkets.com/research/f6c366/data_analysis_in_f) has announced the addition of John Wiley and Sons Ltd's new book "Data Analysis in Forensic Science: A Bayesian Decision Perspective" to their offering.

“Data Analysis in Forensic Science: A Bayesian Decision Perspective”

This is the first text to examine the use of statistical methods in forensic science and bayesian statistics in combination.

The book is split into two parts: Part One concentrates on the philosophies of statistical inference. Chapter One examines the differences between the frequentist, the likelihood and the Bayesian perspectives, before Chapter Two explores the Bayesian decision-theoretic perspective further, and looks at the benefits it carries.

Part Two then introduces the reader to the practical aspects involved: the application, interpretation, summary and presentation of data analyses are all examined from a Bayesian decision-theoretic perspective. A wide range of statistical methods, essential in the analysis of forensic scientific data is explored. These include the comparison of allele proportions in populations, the comparison of means, the choice of sampling size, and the discrimination of items of evidence of unknown origin into predefined populations.

Throughout this practical appraisal there are a wide variety of examples taken from the routine work of forensic scientists. These applications are demonstrated in the ever-more popular R language. The reader is taken through these applied examples in a step-by-step approach, discussing the methods at each stage.

Key Topics Covered:

• Foreword.
• Preface.
• I The Foundations of Inference and Decision in Forensic Science.
• 1 Introduction.
• 2 Scientific Reasoning and Decision Making.
• 3 Concepts of Statistical Science and Decision Theory.
• II Forensic Data Analysis.
• 4 Point Estimation.
• 5 Credible Intervals.
• 7 Sampling.
• 8 Classification of Observations.
• 9 Bayesian Forensic Data Analysis: Conclusions and Implications.
• A Discrete Distributions.
• B Continuous Distributions.
• Bibliography.
• Author Index.
• Subject Index.

For more information visit http://www.researchandmarkets.com/research/f6c366/data_analysis_in_f