Kruschke, J. K. (2011). Doing Bayesian Data Analysis: A Tutorial with R and BUGS. Burlington, MA: Academic Press.

2013 ◽  
Vol 50 (4) ◽  
pp. 469-471 ◽  
Author(s):  
Kimberly F. Colvin
Keyword(s):  
Data Analysis ◽  
Academic Press ◽  
Bayesian Data Analysis

A Bayesian Data Analysis System for the Evaluation of Social Programs

1977 ◽  
Vol 72 (360) ◽  
pp. 711 ◽  
Author(s):  
Ming-Mei Wang ◽  
Melvin R. Novick ◽  
Gerald L. Isaacs ◽  
Dan Ozenne
Keyword(s):  
Data Analysis ◽  
Social Programs ◽  
Bayesian Data Analysis ◽  
Analysis System ◽  
Data Analysis System

scholarly journals Bayesian data analysis in the phonetic sciences: A tutorial introduction

2018 ◽  
Vol 71 ◽  
pp. 147-161 ◽  
Author(s):  
Shravan Vasishth ◽  
Bruno Nicenboim ◽  
Mary E. Beckman ◽  
Fangfang Li ◽  
Eun Jong Kong
Keyword(s):  
Data Analysis ◽  
Bayesian Data Analysis

Mathematics and Statistics in Anesthesiology

2018 ◽  
Author(s):  
Daniel Mortlock
Keyword(s):  
Probability Theory ◽  
Data Analysis ◽  
Survey Design ◽  
Probability Distributions ◽  
Statistical Tests ◽  
Real World Data ◽  
Mathematical Functions ◽  
P Values ◽  
Bayesian Data Analysis ◽  
Probability And Statistics

Mathematics is the language of quantitative science, and probability and statistics are the extension of classical logic to real world data analysis and experimental design. The basics of mathematical functions and probability theory are summarized here, providing the tools for statistical modeling and assessment of experimental results. There is a focus on the Bayesian approach to such problems (ie, Bayesian data analysis); therefore, the basic laws of probability are stated, along with several standard probability distributions (eg, binomial, Poisson, Gaussian). A number of standard classical tests (eg, p values, the t-test) are also defined and, to the degree possible, linked to the underlying principles of probability theory. This review contains 5 figures, 1 table, and 15 references. Keywords: Bayesian data analysis, mathematical models, power analysis, probability, p values, statistical tests, statistics, survey design

Bayesian Data Analysis

2012 ◽  
pp. 423-443 ◽  
Author(s):  
Herbert Hoijtink
Keyword(s):  
Data Analysis ◽  
Bayesian Data Analysis

Bayesian Data Analysis for Animal Scientists

2017 ◽  
Author(s):  
Agustín Blasco
Keyword(s):  
Data Analysis ◽  
Bayesian Data Analysis

scholarly journals The Nested_fit Data Analysis Program

Proceedings ◽  
2019 ◽  
Vol 33 (1) ◽  
pp. 14 ◽  
Author(s):  
Martino Trassinelli
Keyword(s):  
Data Analysis ◽  
Probability Distributions ◽  
Analysis Program ◽  
Large Database ◽  
Data Set ◽  
Lawn Mower ◽  
Bayesian Data Analysis ◽  
Bayesian Evidence ◽  
Spectral Profiles ◽  
Log Normal

We present here Nested_fit, a Bayesian data analysis code developed for investigations of atomic spectra and other physical data. It is based on the nested sampling algorithm with the implementation of an upgraded lawn mower robot method for finding new live points. For a given data set and a chosen model, the program provides the Bayesian evidence, for the comparison of different hypotheses/models, and the different parameter probability distributions. A large database of spectral profiles is already available (Gaussian, Lorentz, Voigt, Log-normal, etc.) and additional ones can easily added. It is written in Fortran, for an optimized parallel computation, and it is accompanied by a Python library for the results visualization.

Bayesian Data Analysis

1995 ◽  
Keyword(s):  
Data Analysis ◽  
Bayesian Data Analysis

Improving our understanding of Bangladesh tropical cyclone risk: decision making insights using kilometre scale numerical modelling and Bayesian data analysis

2020 ◽  
Author(s):  
Hamish Steptoe ◽  
Theo Economou ◽  
Bernd Becker
Keyword(s):  
Decision Making ◽  
Data Analysis ◽  
Numerical Models ◽  
Management Strategies ◽  
Extreme Rainfall ◽  
Analysis Framework ◽  
Bayesian Data Analysis ◽  
Weather And Climate ◽  
Data Gap ◽  
Risk Assessment And Management

<p>We present results from state-of-the-art kilometre scale numerical models of tropical cyclones over Bangladesh.  We demonstrate how the latest generation of numerical models are filling the data gap in regions of the world with sparse observational networks, and compare our results to the latest generation global reanalyses.  We show how an ensemble of simulations expands our understanding of plausible events beyond our limited observations record.  Utilising this ensemble information in a Bayesian data analysis framework, we can robustly estimate prediction intervals for various parameters, such as peak wind speed or extreme rainfall, which when combined with Decision Theory and a loss function offer a coherent data-to-decision framework supporting disaster risk assessment and management strategies. We show how this decision making could be integrated into current global weather and climate forecast ensembles to provide forecasting of hazards and impacts up to 5 days ahead of an event, and in a future climate context.  We end with some thoughts on the ways this could influence the future of risk management and insurance underwriting and the challenges of working with big numerical model datasets.</p>

Sign in / Sign up

Export Citation Format

Share Document