By Phil Woodward

ISBN-10: 1439839549

ISBN-13: 9781439839546

Although the recognition of the Bayesian method of information has been turning out to be for years, many nonetheless ponder it as a bit esoteric, no longer occupied with functional matters, or typically too tough to understand.

Bayesian research Made basic is aimed toward those that desire to practice Bayesian tools yet both aren't specialists or would not have the time to create WinBUGS code and ancillary records for each research they adopt. obtainable to even those that wouldn't oftentimes use Excel, this booklet offers a customized Excel GUI, instantly invaluable to these clients who are looking to manage to speedy practice Bayesian equipment with out being distracted by way of computing or mathematical issues.

From easy NLMs to complicated GLMMs and beyond, Bayesian research Made Simple describes the way to use Excel for an enormous variety of Bayesian versions in an intuitive demeanour obtainable to the statistically savvy consumer. full of proper case reports, this publication is for any info analyst wishing to use Bayesian ways to research their facts, from specialist statisticians to statistically conscious scientists.

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Additional info for Bayesian Analysis Made Simple: An Excel GUI for WinBUGS (Chapman & Hall/CRC Biostatistics Series)

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8 BugsXLA’s Role The WinBUGS package contains many more features than have been illustrated in this section, and anyone wishing to become proficient in its use will need to become familiar with the manual that is built into the program (‘Help: User Manual’ from the menu bar, or Spiegelhalter et al. (2003)), as well as working through some of the examples (‘Help: Examples Vol I and II’). The book by Ntzoufras (2009) is also recommended, as it provides details on every aspect of the WinBUGS package with worked examples of simpler models than those packaged with WinBUGS.

Also, it is very strongly recommended that the user knows how to use WinBUGS to assess convergence of the MCMC run, since BugsXLA does not provide any separate functionality for this purpose. In order to gain this understanding, at least a basic knowledge of WinBUGS is required. 5 onward). 1, in the Bayesian approach inferences are made by conditioning on the data to obtain a posterior distribution for the parameters in the model. The MCMC approach is used to marginalize over the posterior distribution in order to obtain inferences on the main quantities of interest.

7 Identifying and Dealing with Poor Mixing As mentioned above, assessing convergence of the MCMC process is more difficult when the simulated values exhibit strong autocorrelation. 8 shows a history plot of a parameter from a different model and data set that clearly shows such correlation. 9 shows the estimated autocorrelation function. 8 History plot of parameter exhibiting clear autocorrelation. 9 Autocorrelation plot of parameter exhibiting clear autocorrelation. 10 History plot of parameter with one-fifth thinning.

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Bayesian Analysis Made Simple: An Excel GUI for WinBUGS (Chapman & Hall/CRC Biostatistics Series) by Phil Woodward

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