In this chapter, we review basic concepts from probability theory and computational statistics that are fundamental to evolutionary genomics. We provide a very basic introduction to statistical modeling and discuss general principles, including maximum likelihood and Bayesian inference. Markov chains, hidden Markov models, and Bayesian network models are introduced in more detail as they occur ...

This paper applies the tools of computation information geometry [3] – in particular, high dimensional extended multinomial families as proxies for the ‘space of all distributions’ – in the inferentially demanding area of statistical mixture modelling. A range of resultant benefits are noted.

ii Introduction Introduction Why do you need to know the theory behind statistics and probability to be proficient in computational biology? While it is true that software to analyze biological data often comes prepackaged and in a working form (sometimes), it is important to know how these tools work if we are able to interpret the meaning of their results properly. Even more important, often ...

The main task in causal inference is the prediction of the outcome of an intervention. For example, a treatment assigned by a doctor that will change the patient’s heart condition is an intervention. Predicting the change in patient condition is a causal inference task. In general, an intervention is an action taken by an external agent that changes the original values, or the probability distr...

This paper lays the foundations for a new framework for numerically and computationally applying information geometric methods to statistical modelling.

This paper introduces the Bayesian Inference Engine (BIE), a general parallel-optimised software package for parameter inference and model selection. This package is motivated by the analysis needs of modern astronomical surveys and the need to organise and reuse expensive derived data. I describe key concepts that illustrate the power of Bayesian inference to address these needs and outline th...

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Transparent and Efficient I/O for Statistical Computing