Posteriors, conjugacy, and exponential families for completely random measures
We demonstrate how to calculate posteriors for general Bayesian nonparametric priors and likelihoods based on completely random measures (CRMs). We further show how to represent Bayesian nonparametric priors as a sequence of finite draws using a size-biasing approach – and how to represent full Baye...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Bernoulli Society for Mathematical Statistics and Probability
2020
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| Online Access: | https://hdl.handle.net/1721.1/128659 |