Simulating Language 2016, lecture 9 & 10 pre-reading

In the second half of the course, we are going to work with models which treat language learning as a process of inference: a learner observes data and infers a language from that data. Specifically, we are working with models which treat language learning as a process of Bayesian inference: learning involves calculating the posterior probability of a language, based on its prior probability and the likelihood that the observed data was generated by that language. We will generally be working with very simple models, where the learner gets data of a very simple sort and infers one of a small number of possible languages – in fact, the models we will be using are very similar or identical to the coin-tossing models that are discussed in the readings below.

The aim for the pre-reading is to give you a basic understanding of Bayesian inference. Understanding Bayes’ Rule involves a little bit of basic maths, plus some notation for talking about probabilities, conditional probabilities and so on. I have tried to find a couple of relatively basic introductions, you should do at least one of the following two reading options:

  1. For the real basics, read Chapter 1 of Stone (2013). This will equip you with just enough to understand the lectures and do stuff with the code. If you find this OK and want to get a little more in-depth, you could read Chapters 2-4 of the same book, or try option 2 below.
  2. For an introduction that covers virtually everything we need on the course and more, in quite a lot of detail, read Chapters 2-6 of Kruschke (2011). This involves a bit more maths than the first chapter of the Stone book, and is a bit less gentle, but if you are prepared to put the effort in I think it’ll give you a really solid understanding of Bayesian inference and make the stuff we do on the rest of the course fairly straightforward. Note that that book is designed around the R programming language – we aren’t using R, so don’t mess with the R programming exercises at the end of each chapter.

Whichever reading you do, do the post-reading quiz, which is in three parts:

References

Kruschke, J. K. (2011). Doing Bayesian Data Analysis: A Tutorial with R and BUGS. London: Academic Press.

Stone, J. V. (2013). Bayes’ Rule: A Tutorial Introduction to Bayesian Analysis.