Non-Parametric Sampling with Parallel Monte Carlo
c(
person(given = "Simon", family = "Lyddon", role = c("aut"), email = "simon.lyddon@stats.ox.ac.uk"),
person(given = "Miguel", family = "Morin", role = c("aut"), email = "info@turing.ac.uk"),
person(given = "James", family = "Robinson", role = c("aut", "cre"), email = "james.em.robinson@gmail.com"),
person("The Alan Turing Institute", role=c("cph"), email = "info@turing.ac.uk"))
An implementation of a non-parametric statistical model using a
parallelised Monte Carlo sampling scheme. The method implemented in this
package allows non-parametric inference to be regularized for small sample
sizes, while also being more accurate than approximations such as
variational Bayes. The concentration parameter is an effective sample size
parameter, determining the faith we have in the model versus the data. When
the concentration is low, the samples are close to the exact Bayesian
logistic regression method; when the concentration is high, the samples are
close to the simplified variational Bayes logistic regression. The method is
described in full in the paper Lyddon, Walker, and Holmes (2018),
"Nonparametric learning from Bayesian models with randomized objective
functions" <arXiv:1806.11544>.
Simon Lyddon [aut],
Miguel Morin [aut],
James Robinson [aut, cre],
The Alan Turing Institute [cph]

A package including several functions related to the Potts
models.
I am grateful to Dr. Luke Tierney for valuable advice.

A package including several functions related to the Potts models.

There are three sets of functions. The first produces
basic properties of a graph and generates samples from
multinomial distributions to facilitate the simulation
functions (they maybe used for other purposes as well).
The second provides various simulation functions for a
Potts model in Potts, R. B. (1952)
<doi:10.1017/S0305004100027419>.
The third currently includes only one function which
computes the normalizing constant of a Potts model
based on simulation results.