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bdacm_2017's Introduction

Bayesian Data Analysis & Cognitive Modeling

The course introduces main ideas and tools of Bayesian data analysis. We will compare standard and Bayesian approaches to statistical inference. We will also look at Bayesian inference and model comparison for special-purpose cognitive models (with a focus on general cognition and psycholinguistics).

Course notes

  1. How to obtain course material from GitHub
  2. What software to install
  3. What literature to read

Schedule & slides

NB: slides are HTML files; use arrow keys to navigate.

n date topic reading (main) extra info
1 25/4 overview & formalities
2 28/4 handling & plotting data in R R for Data Science 3, 5, 12, 18, 21
3 2/5 primer on probability & "classical" statistics Kruschke ch. 4
4 5/5 p-problems & Rmarkdown Wagenmakers (2007), R for Data Science IV Brechtbau 0.35
5 9/5 intro to BDA Krushke ch. 5 & 6
6 12/5 MCMC sampling Kruschke ch. 7 HW1 due
7 16/5 JAGS Kruschke ch. 8
8 19/5 practice: parameter inference 1 Lee & Wagenmakers ch. 3, 4
9 23/5 hierarchical modeling Kruschke ch. 9
10 26/5 practice: parameter inference 2 Lee & Wagenmakers ch. 5, 6 HW2 due
11 30/5 theory: model comparison Kruschke ch 10, Lee & Wagenmakers ch. 7
12 2/6 practice: model comparison Lee & Wagenmakers ch. 7, 8
-- -- pentecoast --
13 13/6 computing Bayes factors --
14 16/6 Bayes in philosophy of science -- HW3 due
-- 20/6 no class
15 23/6 computing Bayes factors 2 --
16 27/6 estimation, comparison & criticism Kruschke 11, 12
17 30/6 practice: Generalized Context Model Lee & Wagenmakers ch. 17 HW4 due
18 4/7 Stan Kruschke ch. 14, Stan manual
19 7/7 practice: cognitive models 2 Lee & Wagenmakers ch. 11,
20 11/7 generalized linear model Kruschke ch 15, 16, 17
21 14/7 more on the GLM Kruschke ch 16-22
22 18/7 mixed models & LOO Sorensen et al. (2016)
23 21/7 rounding off, project topics
28/7 HW5 due

bdacm_2017's People

Contributors

michael-franke avatar

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