The continuous-time Markov chain (CTMC) is the mathematical workhorse of evolutionary
biology. Learning CTMC model parameters using modern, gradient-based methods requires …

Abstract Parallel Markov Chain Monte Carlo (pMCMC) algorithms generate clouds of
proposals at each step to efficiently resolve a target probability distribution. We build a …

We propose a novel hybrid quantum computing strategy for parallel MCMC algorithms that
generate multiple proposals at each step. This strategy makes the rate-limiting step within …

Abstract Multivariate Hawkes Processes (MHPs) are a class of point processes that can
account for complex temporal dynamics among event sequences. In this work, we study the …

Proceeding from the fact that fractional systems can better characterize the virological
properties than the ordinary formulation, in the present study, we treat a Caputo fractional …

We present the simplicial sampler, a class of parallel MCMC methods that generate and
choose from multiple proposals at each iteration. The algorithm's multiproposal randomly …

The Hawkes process is a self-exciting Poisson point process, characterised by a conditional
intensity function. Since its introduction fifty years ago, it has been the subject of numerous …

Neural marked temporal point processes have been a valuable addition to the existing
toolbox of statistical parametric models for continuous-time event data. These models are …

Many modern spatio-temporal data sets, in sociology, epidemiology or seismology, for
example, exhibit self-exciting characteristics, triggering and clustering behaviors both at the …

Hawkes stochastic point process models have emerged as valuable statistical tools for
analyzing viral contagion. The spatiotemporal Hawkes process characterizes the speeds at …