This is another of my occasional postings working through the exercises given at the end of the chapters in ‘Bayesian analysis with Stata’. Chapter 3 introduces the Metropolis-Hastings sampler and so enables us to tackle any small sized Bayesian analysis. For larger problems this method can be too slow and faster algorithms are needed. Despite […]

# Bayesian Analysis with Stata

## Using R with Stata: Part IV

This is the last in a series of posting about using conducting advanced statistical analyses in Stata by sending a job to R and then reading the results back into Stata. I have spent a while on this topic and although I believe it to be very important, next time I want to return to […]

## Using R with Stata: Part III

This is another in a series of posting about using conducting advanced statistical analyses in Stata by sending a job to R and then reading the results back into Stata. Our task for testing this process is to use the R package DPpackage to fit a Bayesian Dirichlet process mixture (DPM) model for smoothing a […]

## Using R with Stata: Part II

This is the second in a series of posting about conducting advanced statistical analyses in Stata by sending a job to R and then reading the results back into Stata. Our task for testing this process is to use the R package, DPpackage, to fit a Bayesian Dirichlet process mixture (DPM) model for smoothing a scatter plot. […]

## Using R with Stata: Part I

In the posting before last I mentioned that there are many R packages that perform advanced statistical analyses for which there is no equivalent Stata command. In particular, I referred to the R package, DPpackage, that fits a range of Bayesian models with Dirichlet process priors. Then in my last posting I introduced a problem […]

## Stata and Advanced Statistical Methods

My recent postings have been on Bayesian non-parametric analysis with Dirichlet processes and they have raised a couple of questions in my mind that I should now like to discuss. Is Stata a suitable vehicle for advanced statistical analysis? and if so, Who should do the programming? Dirichlet processes (DPs) provide very flexible Bayesian priors with […]

## Gibbs Sampling with a Dirichlet Process

This is the third in a series of postings on the use of Dirichlet processes for non-parametric Bayesian analysis and their implementation in Stata. In this posting I will create a Stata program that fits a Bayesian model that incorporates a Dirichlet process prior. As my example I will model the peak expiratory flow measurements […]

## Selecting a Dirichlet Process Prior

This is a continuation my previous posting on non-parametric Bayesian analysis and this time I will try to show how a Dirichlet process can be used to create a family of distributions that provide much more flexible priors than the standard options such as the normal or gamma. Last time we saw how we can represent a distribution […]

## Non-parametric Bayesian Analysis

On the internet there is a host of sites that describe the mathematics of Dirichlet processes, but very few of them try to explain the ideas behind the algebra. Dirichlet process methods are very important for modern Bayesian analysis and raise a number of interesting programming issues when they are to be implemented in Stata, […]

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