3 papers
stat.CO2021
The Apogee to Apogee Path Sampler
Chris Sherlock, Szymon Urbas, Matthew Ludkin
Amongst Markov chain Monte Carlo algorithms, Hamiltonian Monte Carlo (HMC) is often the algorithm of choice for complex, high-dimensional target distributions; however, its efficie…
stat.ME2019
Inference for a generalised stochastic block model with unknown number of blocks and non-conjugate edge models
Matthew Ludkin
The stochastic block model (SBM) is a popular model for capturing community structure and interaction within a network. Network data with non-Boolean edge weights is becoming commo…
stat.CO2019
Hug and Hop: a discrete-time, non-reversible Markov chain Monte-Carlo algorithm
Matthew Ludkin, Chris Sherlock
We introduced the Hug and Hop Markov chain Monte Carlo algorithm for estimating expectations with respect to an intractable distribution. The algorithm alternates between two kerne…