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 widely used is the Expectation Maximization, or

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(x) = P(x_L,x_{L-1},...,x_

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 post. For my first post, I'll be

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. This can get you into trouble though,

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 Data Analysis text, and again, this explanation

Estimating the Sample Mean of Normally Distributed Data As I'm going through a reading course with Xin He in which I'm going through a few chapters in the book Bayesian Data Analysis . I've gotten to the chapter on multi-parameter models, and

Programming IO in R vs Python One of the perennial annoyances of modern scientific computing is how slow it is to read in large files. Bioinformaticians have a fetish for plain text, and this, while generally regarded as a