The EM Algorithm
In the world of model-based inference, unobserved parameters give rise to observed data. There are a number of ways of estimating these parameters; among the most »
Hidden Markov Model Algorithms
Markov Models Transition probabilities are written as: $$a_{st} = P(x_i = t | x_{i-1} =s)$$ We can write the probability of any sequence as : $$P( »
Inferring population admixture
In the process of preparing for my preliminary exam, I thought it might be useful to summarize some of the things I've learned with a blog »
Vectorizing arguments in R
R exists in a funny space between being a functional and procedural language. Unlike languages like LISP, R is perfectly happy to interpret your for loop. »
Hierarhical Modeling
The Setup Let's look at an example of making inference using hierarchical modeling. The example we'll use is the rat tumor data set from Gelman's Bayesian »