Intro to WinBUGS / JAGS
Plan for the Jan. 31 & Feb. 2 classes; feel free to bring your laptops to class and work through the scripts on your own as we go through them in class:

First script: bayeswinbugsjags1.r; contents:
 the mean model: simulated data, R analysis, WinBUGS / JAGS analysis
 the structure of WinBUGS …
Intro to Bayesian inference and MCMC (part 2)
Plan for the Jan. 24 & 26 classes:
 wrap up examples of inference for binomial proportions with conjugate Beta priors and with griddiscretized priors
 intro to Bayes for cognitive science; slides: introbayes2.pdf
 introduction to Markov Chain Monte Carlo (MCMC) and the Metropolis family of algorithms bayesMCMCintro.r; on …
Cox (1946) and Jaynes (2003)
Intro to Bayesian inference (part 1)
Plan for the Jan. 17 class:
 intro to Bayesian inference (part 1); slides: introbayes1.pdf
 examples of inference for binomial proportions with conjugate (Beta) priors — based on Kruschke (2011), chapter 5: examplesBernBeta.r; to run the examples, you will need the following 2 files: BernBeta.R and HDIofICDF.R
 examples …
Intro to probability — slides
The slides we discussed on Jan. 12 in the semantics seminar are available here. You can also take a look at Kruschke (2011), chapters 3 and 4.
read more“Doing Bayesian Data Analysis”  Now in JAGS
John Kruschke has created JAGS versions of all the programs in “Doing Bayesian Data Analysis”.
Unlike BUGS, JAGS runs on MacOS, Linux, and Windows. JAGS has other features that make it more robust and userfriendly than BUGS. I recommend that you use the JAGS versions of the programs.
For more …
read moreSeminar: Statistical & Cognitive Modeling for Formal Semantics
Winter 2012 Seminar in Semantics (Linguistics, UCSC):
 Statistical & Cognitive Modeling for Formal Semantics
See the syllabus and AB’s teaching page for more information.
read more[Fall 2011] Ordinal probit ‘ttest’
An introduction to ordinal probit regression: simulated data for an ordinal probit ‘ttest’, i.e., an ordinal probit regression with only one predictor (a factor with 2 levels); frequentist analysis in R using the “ordinal” package; Bayesian analysis using WinBUGS and JAGS: ordinalprobitregression.r.
read more[Spring 2011] Intro to Bayesian data analysis
A very nice compact argument for Bayesian methods can be found in John Kruschke‘s Bayesian Data Analysis, WIREs Cognitive Science 1, 658676. Here’s the very beginning of the article (followed by a section entitled “The road to NHST is paved with good intentions”):
read moreThis brief article assumes that …
[Spring 2011] GLMs & GLMMs (ctd.)
Plan: linear regression wrapup; introduce logistic regression models, maximum likelihood estimation of their parameters and the frequentist quantification of the uncertainty associated with those estimates; introduce the Bayesian approach to regression models with the goal of having a flexible way to estimate all sorts of logistic regression models, including binary …
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