Information for EDMS Fall 2006, "Bayesian Inference in Measurement"

These are input files for WinBUGS examples. Save to your computer, open from WinBUGS.

* Variables Doodle file for exploring distributions.

* CoinDoodle Doodle file for two unreliable reports of a coin flip.

* Coin10 Doodle file for ten unreliable reports of a coin flip, illustrating the use of a plate.

* BetaBinomial Example from class on updating with conjugate distributions--specifically, updating a beta prior with a binomial likelihood, to get a beta posterior.

* Simple regression Linear regression.

* Normal doodle.

* Normal code.

* CTT Code for in-class CTT example.

* CTTshadow Code for PPMC with CTT example.

* Rasch example Code for in-class Rasch model example.

* 3PL Code for in-class three-parameter logistic IRT example.

* PPMC Posterior predictive distributions for model checking in IRT. Thanks to Roy Levy.

* TwoSkillsDoodle Doodle for estimating population and conditional probabilities from data, in Two Skills problem from Assignment 3.

* TwoSkillsCode WinBUGS code and data for estimating population and conditional probabilities in Two Skills problem from Assignment 3.

* Cheating: Manifest Univariate Model

* Cheating: Manifest Multivariate Model

* Cheating: Latent Class Model with Uniform(0,.5) prior on class membership

* Cheating: Latent Class Model with Uniform(0,1) prior on class membership

* Simple regression

* Standardized regression

* Multivariate standardized regression

* One-dimensional factor analysis

* Balance Beam -- five attempts WinBUGS code.

* Balance Beam -- two attempts data file.

* MovieData Code for example used in the WinBUGS movie example.

* Proficiency1: 100 cases, Proficiency observed directly

* Proficiency2: 100 cases, Proficiency observed directly + three History-taking observations

* Proficiency3: 100 cases, Proficiency not observed + three History-taking observations

* Graded response data : 165 cases, three items with {1,2,3} responses

* Graded response spreadsheet : Calculating cumulative and category probabilities for a three response category item.

* In-class graded response model. Constructed during the November 13 class period. Warning: Offerred "as is"--has not been tested.

* CTT-DIC1, CTT-DIC2, CTT-DIC3, CTT-DIC4.WinBUGS setups for classical test theory model comparisons in presentation on DIC.