Bayesian

Bayesian statistics blogs

Converting combination of random variables to hierarchical form for JAGS (BUGS, Stan, etc.)

August 20, 2014
By
Converting combination of random variables to hierarchical form for JAGS (BUGS, Stan, etc.)

An emailer asks:Hi, John. Long-time listener, first-time caller... I have a model that says X is a function of three (independent) random variables: X ~ normal(mu, sigma) / uniform(a,a+b) - beta(v,w) and I also have N random samples of X. Can I use JAG...

Read more »

How to use MCMC posterior as prior for future data

August 15, 2014
By

An emailer writes:Dear Prof. Kruschke,Hello. My name is ... and I am ... . I'm trying to apply Bayesian theorem in developing a model of ... . I used your code to estimate posterior distribution without any trouble. Here is my question. Would you ki...

Read more »

Stopping and testing intentions in p values

August 12, 2014
By

An emailer asks:I am really interested in Bayesian analysis, but I don't get the issue of sampling intention being so important in frequentist t-tests; if you have 60 values you have 60 values surely - why does your intention matter? The computer does ...

Read more »

Prior for normality (df) parameter in t distribution

August 8, 2014
By
Prior for normality (df) parameter in t distribution

A routine way to describe outliers in metric data is with a heavy-tailed t distribution instead of with a normal distribution. The heaviness of the tails is governed by a normality parameter, ν, also called the df parameter. What is a reasonable prior...

Read more »

Plot with ggplot2, interact, collaborate, and share online

July 31, 2014
By
Plot with ggplot2, interact, collaborate, and share online

Editor’s note: This is a guest post by Marianne Corvellec from Plotly. This post is based on an interactive Notebook (click to view) she presented at the R User Conference on July 1st, 2014. Plotly is a platform for making, editing, and sharing graphs. If you are used to making plots with ggplot2, you can […]

Read more »

Tuning particle MCMC algorithms

June 8, 2014
By
Tuning particle MCMC algorithms

Several papers have appeared recently discussing the issue of how to tune the number of particles used in the particle filter within a particle MCMC algorithm such as particle marginal Metropolis Hastings (PMMH). Three such papers are: Doucet, Arnaud, Michael Pitt, and Robert Kohn. Efficient implementation of Markov chain Monte Carlo when using an unbiased […]

Read more »

Tuning particle MCMC algorithms

June 8, 2014
By
Tuning particle MCMC algorithms

Several papers have appeared recently discussing the issue of how to tune the number of particles used in the particle filter within a particle MCMC algorithm such as particle marginal Metropolis Hastings (PMMH). Three such papers are: Doucet, Arnaud, Michael Pitt, and Robert Kohn. Efficient implementation of Markov chain Monte Carlo when using an unbiased […]

Read more »

Potpourri 2: more misc emails regarding doing Bayesian data analysis

April 1, 2014
By

More miscellaneous communications with readers. (I have omitted all the salutations and pleasantries to save space here, but most correspondents do begin and end their messages with introductions and salutations that I truly appreciate.) Again, apologi...

Read more »

Potpourri of recent inquires about doing Bayesian data analysis

March 22, 2014
By

I get a lot of email asking about the book and the blog. Too often, I say to myself, "That's interesting. I'll have to think about it and reply later." And then later never comes. My apologies to everyone to whom I have not yet replied. Below is a samp...

Read more »

Bayesian estimation and precision as the goal for data collection (expanded)

March 15, 2014
By

Precision as the goal for data collection, talk at U.C. Irvine, March 14, 2014.Part 1: Rejecting null is not enough, also need estimate and precision. Bayesian estimation supersedes confidence intervals and "the new statistics". Part 2: Two Bayesian w...

Read more »


Subscribe

Email:

  Subscribe