I am trying to sample from a Dirichlet distribution, where some of the shape parameters are very small. To do so, the algorithm samples each component individually from a Gamma(k,1) distribution where k is the shape parameter for that component of the Dirichlet. In principle, this should always return a positive number (as the Dirichlet is defined). However, if k is very small, it will return zero:
In [157]: np.random.gamma(1e-1) Out[157]: 4.863866491339177e-06 In [158]: np.random.gamma(1e-2) Out[158]: 2.424451829710714e-57 In [159]: np.random.gamma(1e-3) Out[159]: 5.1909861689757784e-197 In [160]: np.random.gamma(1e-4) Out[160]: 0.0 In [161]: np.random.gamma(1e-5) Out[161]: 0.0 What is the best way to deal with this? Thanks! Uri ................................................................................... Uri Laserson Graduate Student, Biomedical Engineering Harvard-MIT Division of Health Sciences and Technology M +1 917 742 8019 laser...@mit.edu
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