Search Results for author: Jack Baker

Found 3 papers, 3 papers with code

Large-Scale Stochastic Sampling from the Probability Simplex

1 code implementation NeurIPS 2018 Jack Baker, Paul Fearnhead, Emily B. Fox, Christopher Nemeth

Unfortunately, many popular large-scale Bayesian models, such as network or topic models, require inference on sparse simplex spaces.

Bayesian Inference Topic Models

sgmcmc: An R Package for Stochastic Gradient Markov Chain Monte Carlo

1 code implementation2 Oct 2017 Jack Baker, Paul Fearnhead, Emily B. Fox, Christopher Nemeth

To do this, the package uses the software library TensorFlow, which has a variety of statistical distributions and mathematical operations as standard, meaning a wide class of models can be built using this framework.

Bayesian Inference

Control Variates for Stochastic Gradient MCMC

1 code implementation16 Jun 2017 Jack Baker, Paul Fearnhead, Emily B. Fox, Christopher Nemeth

These methods use a noisy estimate of the gradient of the log posterior, which reduces the per iteration computational cost of the algorithm.

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