Principles of Bayesian Methods in Data Analysis

Michael Paul Krystek

doi:10.4028/www.scientific.net/KEM.437.3

Paper Titles

Preface

Sponsors and Committees

List of Reviewers

Principles of Bayesian Methods in Data Analysis
p.3

Dynamic Pressure Calibration of Pressure Sensors Using Liquid Step Pressure Generator
p.8

Topical Tasks of Metrology due to Measuring Instruments Computerization
p.13

Multivariant System of Perspectives
p.18

Coherent Optical Method for Studies of Nanoscale Objects in Liquid Media Based upon Spatial Averaging of Data
p.25

Measurement and Visualization of Dynamics of Piezoelectric Microcantilever
p.30

HomeKey Engineering MaterialsKey Engineering Materials Vol. 437Principles of Bayesian Methods in Data Analysis

Principles of Bayesian Methods in Data Analysis

Abstract:

Bayesian statistics provides a powerful tool for the analysis of data. The methods are flexible enough to permit a realistic modelling of complex measurements. Prior information about the experiment, as well as knowledge from other sources can be used in a natural way. All relevant quantities concerning the measurement, as e. g. the expected values and their associated uncertainties are obtained from probability density functions. Bayesian data analysis strictly follows the rules of probability theory, thus ensuring that the procedure is free of inconsistencies and is in accordance with the Guide to the Expression of Uncertainty in Measurement (GUM).

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Key Engineering Materials (Volume 437)

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3-7

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.437.3

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Online since:

May 2010

Authors:

Michael Paul Krystek

Keywords:

Bayesian Statistic, Data Analysis, Measurement Uncertainty

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