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Found 5 papers, 2 papers with code
Particle filter with rejection control and unbiased estimator of the marginal likelihood
While the variance reducing properties of rejection control are known, there has not been (to the best of our knowledge) any work on unbiased estimation of the marginal likelihood (also known as the model evidence or the normalizing constant) in this type of particle filter.
Probabilistic programming for birth-death models of evolution using an alive particle filter with delayed sampling
Probabilistic programming languages (PPLs) give phylogeneticists a new and exciting tool: their models can be implemented as probabilistic programs with just a basic knowledge of programming.
Automated learning with a probabilistic programming language: Birch
We introduce a formal description of models expressed as programs, and discuss some of the ways in which probabilistic programming languages can reveal the structure and form of these, in order to tailor inference methods.
Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs
For inference with Sequential Monte Carlo, this automatically yields improvements such as locally-optimal proposals and Rao-Blackwellization.
Anytime Monte Carlo
Monte Carlo algorithms simulate some prescribed number of samples, taking some random real time to complete the computations necessary.
