Sampling from multimodal distributions using tempered Hamiltonian transitions

1 code implementation • 12 Nov 2021 • Joonha Park

However, HMC struggles when the target distribution is multimodal, because the maximum increase in the potential energy function (i. e., the negative log density function) along the simulated path is bounded by the initial kinetic energy, which follows a half of the $\chi_d^2$ distribution, where d is the space dimension.

A tutorial on spatiotemporal partially observed Markov process models via the R package spatPomp

1 code implementation • 4 Jan 2021 • Kidus Asfaw, Joonha Park, Allister Ho, Aaron A. King, Edward Ionides

A model of this form is called a spatiotemporal partially observed Markov process (SpatPOMP).

Methodology Computation

Markov chain Monte Carlo algorithms with sequential proposals

1 code implementation • 15 Jul 2019 • Joonha Park, Yves F. Atchadé

We explore a general framework in Markov chain Monte Carlo (MCMC) sampling where sequential proposals are tried as a candidate for the next state of the Markov chain.

A guided intermediate resampling particle filter for inference on high dimensional systems

1 code implementation • 28 Aug 2017 • Joonha Park, Edward L. Ionides

We obtain theoretical results showing improved scaling of a GIRF algorithm, relative to widely used particle filters, as the model dimension increases.

Methodology