%0 Journal Article %1 LI2019128 %A Li, Yuelin %A Schofield, Elizabeth %A Gönen, Mithat %D 2019 %J Journal of Mathematical Psychology %K bayesian probability tutorials %P 128 - 144 %R https://doi.org/10.1016/j.jmp.2019.04.004 %T A tutorial on Dirichlet process mixture modeling %U http://www.sciencedirect.com/science/article/pii/S0022249618301068 %V 91 %X Bayesian nonparametric (BNP) models are becoming increasingly important in psychology, both as theoretical models of cognition and as analytic tools. However, existing tutorials tend to be at a level of abstraction largely impenetrable by non-technicians. This tutorial aims to help beginners understand key concepts by working through important but often omitted derivations carefully and explicitly, with a focus on linking the mathematics with a practical computation solution for a Dirichlet Process Mixture Model (DPMM)—one of the most widely used BNP methods. Abstract concepts are made explicit and concrete to non-technical readers by working through the theory that gives rise to them. A publicly accessible computer program written in the statistical language R is explained line-by-line to help readers understand the computation algorithm. The algorithm is also linked to a construction method called the Chinese Restaurant Process in an accessible tutorial in this journal (Gershman and Blei, 2012). The overall goals are to help readers understand more fully the theory and application so that they may apply BNP methods in their own work and leverage the technical details in this tutorial to develop novel methods.

@article{LI2019128, abstract = {Bayesian nonparametric (BNP) models are becoming increasingly important in psychology, both as theoretical models of cognition and as analytic tools. However, existing tutorials tend to be at a level of abstraction largely impenetrable by non-technicians. This tutorial aims to help beginners understand key concepts by working through important but often omitted derivations carefully and explicitly, with a focus on linking the mathematics with a practical computation solution for a Dirichlet Process Mixture Model (DPMM)—one of the most widely used BNP methods. Abstract concepts are made explicit and concrete to non-technical readers by working through the theory that gives rise to them. A publicly accessible computer program written in the statistical language R is explained line-by-line to help readers understand the computation algorithm. The algorithm is also linked to a construction method called the Chinese Restaurant Process in an accessible tutorial in this journal (Gershman and Blei, 2012). The overall goals are to help readers understand more fully the theory and application so that they may apply BNP methods in their own work and leverage the technical details in this tutorial to develop novel methods.}, added-at = {2020-01-27T16:33:42.000+0100}, author = {Li, Yuelin and Schofield, Elizabeth and Gönen, Mithat}, biburl = {https://www.bibsonomy.org/bibtex/2aaacdde85ba47055c4d2c9b096297ecd/kirk86}, description = {A tutorial on Dirichlet process mixture modeling - ScienceDirect}, doi = {https://doi.org/10.1016/j.jmp.2019.04.004}, interhash = {c3171ce91573ea8135086936b1235af3}, intrahash = {aaacdde85ba47055c4d2c9b096297ecd}, issn = {0022-2496}, journal = {Journal of Mathematical Psychology}, keywords = {bayesian probability tutorials}, pages = {128 - 144}, timestamp = {2020-01-27T16:33:42.000+0100}, title = {A tutorial on Dirichlet process mixture modeling}, url = {http://www.sciencedirect.com/science/article/pii/S0022249618301068}, volume = 91, year = 2019 }