A toolbox for computing with 1-D Gaussian mixture models (gmm1). This code is generally fast but there is space for further improvement (e.g., improved vectorization).
gmm1cdf.m
: gmm1 cumulative distribution function (cdf)gmm1ent.m
: gmm1 differential entropy (numerically estimated)gmm1max.m
: Find the global maximum (mode) of gmm1gmm1max_n2.m
: Find the global maximum (mode) of gmm1 with 2 components (faster thangmm1max.m
)gmm1moments.m
: Central moments of gmm1 (mean, variance, skewness, excess kurtosis)gmm1pdf.m
: gmm1 probability density function (pdf)gmm1prod.m
: Product of two gmm1gmm1rnd.m
: Random draw from gmm1 (not optimal, needs recoding)isgmm1.m
: Returns true for a gmm1 struct
This toolbox was created for and extensively used in the following publications (please consider citing them if you use this toolbox):
- Acerbi, L., Vijayakumar, S. & Wolpert, D. M. (2014). On the Origins of Suboptimality in Human Probabilistic Inference, PLoS Computational Biology 10(6): e1003661.
- Acerbi, L., Ma, W. J. & Vijayakumar, S. (2014). A Framework for Testing Identifiability of Bayesian Models of Perception, Proc. Advances in Neural Information Processing Systems (NIPS ’14), Montreal, Canada.