Category Archives: BUGS

“Grid shading by simulated annealing” [Martyn Plummer]

Posted on 9 February 2016 by

Source: Grid shading by simulated annealing (or what I did on my holidays), aka “fun with GCHQ job adverts”, by Martyn Plummer, developer of JAGS. Excerpt: I wanted to solve the puzzle but did not want to sit down with … Continue reading →

Posted in approximate Bayesian computation, Bayesian, Bayesian inversion, Boltzmann, BUGS, Christian Robert, Gibbs Sampling, JAGS, likelihood-free, Markov Chain Monte Carlo, Martyn Plummer, mathematics, maths, MCMC, Monte Carlo Statistical Methods, optimization, probabilistic programming, SPSA, stochastic algorithms, stochastic search | Leave a comment

Bayesian change-point analysis for global temperatures, 1850-2010

Posted on 2 March 2015 by

Professor Peter Congdon reports on two Bayesian models for global temperature shifts in his textbook, Applied Bayesian Modelling, as “Example 6.12: Global temperatures, 1850-2010”, on pages 252-253. A direct link is available online. The first is apparently original with Congdon, … Continue reading →

Posted in Bayes, Bayesian, BUGS, climate, climate change, environment, forecasting, information theoretic statistics, mathematics, MCMC, meteorology, rationality, reasonableness, statistics, stochastic algorithms, Uncategorized | 1 Comment

Christian Robert on the amazing Gibbs sampler

Posted on 19 February 2015 by

Professor Christian Robert remarks on the amazing Gibbs sampler. Implicitly he’s also underscoring the power of properly done Bayesian computational analysis. For here we have a problem with a posterior distribution having two strong modes, so a point estimate, like … Continue reading →

Posted in Bayes, Bayesian, BUGS, Gibbs Sampling, JAGS, mathematics, maths, MCMC, probabilistic programming, rationality, statistics, stochastic algorithms, stochastic search | Leave a comment

An equation-free introduction to Bayesian inference

Posted on 7 December 2014 by

By Tomoharu Eguchi from 2008: “An Introduction to Bayesian Statistics Without Using Equations“.

Posted in Bayes, Bayesian, BUGS, JAGS, mathematics, mathematics education, maths, probabilistic programming, rationality, reasonableness, science education, statistics | Leave a comment

Blind Bayesian recovery of components of residential solid waste tonnage from totals data

Posted on 8 September 2014 by

This is a sketch of how maths and statistics can do something called blind source separation, meaning to estimate the components of data given only their totals. Here, I use Bayesian techniques for the purpose, sometimes called Bayesian inversion, using … Continue reading →

Posted in Bayesian, BUGS, conservation, consumption, engineering, environment, Gibbs Sampling, JAGS, mathematics, maths, MCMC, MSW, politics, probabilistic programming, R, rationality, recycling, statistics, stochastic algorithms, stochastic search | Leave a comment

“The joy and martyrdom of trying to be a Bayesian”

Posted on 27 August 2014 by

Bayesians have all been there. Some of us don’t depend upon producing publications to assure our pay, so we less have the pressure of pleasing peer reviewers. Nonetheless, it’s all reacting to “What the hell are you doing? I don’t … Continue reading →

Posted in Bayesian, BUGS, Gibbs Sampling, JAGS, MCMC, optimization, probabilistic programming, R, rationality, reasonableness, risk, SPSA, statistics, stochastic algorithms, stochastic search | Leave a comment

How fast is JAGS?

Posted on 27 August 2014 by

How fast is JAGS?.

Posted in BUGS, engineering, Gibbs Sampling, JAGS, mathematics, maths, MCMC, probabilistic programming, R, statistics, stochastic algorithms | Leave a comment

A recap of Craven’s Bayesian location search for the Scorpion

Posted on 15 March 2014 by

… in the context of trying to locate Malaysian Airlines Flight 370: See the story at http://www.nytimes.com/2014/03/15/science/earth/us-navy-strategists-have-a-long-history-of-finding-the-lost.html Here is a PowerPoint presentation from 2005 from Walter Stromquist giving some of the technical details about under water searches: Stromquist–BayesianSearch2005 LCDR Kyle Caudle … Continue reading →

Posted in Bayesian, BUGS, WHOI | Tagged , , , | Leave a comment

The zero-crossings trick for JAGS: Finding roots stochastically

Posted on 17 February 2014 by

BUGS has a “zeros trick” (Lund, Jackson, Best, Thomas, Spiegelhalter, 2013, pages 204-206; see also an online illustration) for specifying a new distribution which is not in the standard set. The idea is to couple an invented-for-the-moment Poisson density to … Continue reading →

Posted in Bayesian, BUGS, education, forecasting, Gibbs Sampling, JAGS, mathematics, MCMC, probabilistic programming, R, statistics, stochastic search | Tagged , , | 4 Comments