# References

## Bayesian statistics and information theory

For an introduction to Bayesian statistics, information theory, and Markov
chain Monte Carlo (MCMC), David MacKay's "Information Theory, Inference and
Learning Algorithms" ^{1} is an excellent choice and it's available for
free.

^{1}

MacKay, D. J., & Mac Kay, D. J. (2003). Information theory, inference and learning algorithms. Cambridge university press. (PDF)

## Dirichlet process mixture models

For an introduction to infinite mixture models via the Dirichlet process, Carl
Rasumssen's "*The infinite Gaussian mixture model*"^{2} provides an
introduction to the model; and Radford Neal's "*Markov chain sampling methods
for Dirichlet process mixture models*"^{3} provides an introduction to
basic MCMC methods. When I was learning Dirichlet process mixture models, I
found Frank Wood and Michael Black's "*A nonparametric Bayesian alternative to
spike sorting*" ^{4} extremely helpful. Because its target audience
is applied scientists it lays things out more simply and completely than a
manuscript aimed at statisticians or computer scientists might.

^{2}

Rasmussen, C. (1999). The infinite Gaussian mixture model. Advances in neural information processing systems, 12. (PDF)

^{3}

Neal, R. M. (2000). Markov chain sampling methods for Dirichlet process mixture models. Journal of computational and graphical statistics, 9(2), 249-265. (PDF)

^{4}

Wood, F., & Black, M. J. (2008). A nonparametric Bayesian alternative to spike sorting. Journal of neuroscience methods, 173(1), 1-12. (PDF)

## Probabilistic cross-categorization (PCC)

For a compact explanation designed for people unfamiliar with Bayesian
statistics, see Shafto, et al ^{5}. This work is targeted at
psychologists and demonstrates PCC's power to model human cognitive
capabilities. For a incredibly in-dept overview with loads of math, use cases,
and examples, see Mansinghka et al ^{6}.

^{5}

Shafto, P., Kemp, C., Mansinghka, V., & Tenenbaum, J. B. (2011). A probabilistic model of cross-categorization. Cognition, 120(1), 1-25.(PDF)

^{6}

Mansinghka, V., Shafto, P., Jonas, E., Petschulat, C., Gasner, M., & Tenenbaum, J. B. (2016). Crosscat: A fully bayesian nonparametric method for analyzing heterogeneous, high dimensional data. (PDF)