English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The need to calibrate increasingly complex statistical models requires apersistent effort for further advances on available, computationally intensiveMonte Carlo methods. We study here an advanced version of familiar Markov ChainMonte Carlo (MCMC) algorithms that sample from target distributions defined aschange of measures from Gaussian laws on general Hilbert spaces. Such a modelstructure arises in several contexts: we focus here at the important class ofstatistical models driven by diffusion paths whence the Wiener processconstitutes the reference Gaussian law. Particular emphasis is given onadvanced Hybrid Monte-Carlo (HMC) which makes large, derivative-driven steps inthe state space (in contrast with local-move Random-walk-type algorithms) withanalytical and experimental results. We illustrate it's computationaladvantages in various diffusion processes and observation regimes; examplesinclude stochastic volatility and latent survival models. In contrast withtheir standard MCMC counterparts, the advanced versions have mesh-free mixingtimes, as these will not deteriorate upon refinement of the approximation ofthe inherently infinite-dimensional diffusion paths by finite-dimensional onesused in practice when applying the algorithms on a computer.

Advanced MCMC Methods for Sampling on Diffusion Pathspace