Plan for the Jan. 24 & 26 classes:

1. wrap up examples of inference for binomial proportions with conjugate Beta priors and with grid-discretized priors
2. intro to Bayes for cognitive science; slides: intro-bayes-2.pdf
3. introduction to Markov Chain Monte Carlo (MCMC) and the Metropolis family of algorithms bayes-MCMC-intro.r; on …

The slides for Robert Henderson’s excellent presentation of Cox (1946) (“Probability, Frequency and Reasonable Expectation”) and Jaynes (2003) (“Probability Theory: The Logic of Science”), chapters 1 and 2 are available here.

Plan for the Jan. 17 class:

1. intro to Bayesian inference (part 1); slides: intro-bayes-1.pdf
2. examples of inference for binomial proportions with conjugate (Beta) priors — based on Kruschke (2011), chapter 5: examples-Bern-Beta.r; to run the examples, you will need the following 2 files: BernBeta.R and HDIofICDF.R
3. examples …

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.

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 user-friendly than BUGS. I recommend that you use the JAGS versions of the programs.

For more …

Winter 2012 Seminar in Semantics (Linguistics, UCSC):

• Statistical & Cognitive Modeling for Formal Semantics

See the syllabus and AB’s teaching page for more information.

An introduction to ordinal probit regression: simulated data for an ordinal probit ‘t-test’, 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: ordinal-probit-regression.r.

A very nice compact argument for Bayesian methods can be found in John Kruschke‘s Bayesian Data Analysis, WIREs Cognitive Science 1, 658-676. Here’s the very beginning of the article (followed by a section entitled “The road to NHST is paved with good intentions”):

This brief article assumes that …

Plan: linear regression wrap-up; 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 …

A script introducing basic lexical semantics, syntax and compositional semantics notions and tools with NLTK: NLTK-syn-sem.py, Dowty-et-al.cfg, mygrammar.cfg.