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/

Zendy Plus

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

Zendy Tools

Zendy Open

Hamiltonian Monte Carlo is a prominent Markov Chain Monte Carlo algorithm,which employs symplectic integrators to sample from high dimensional targetdistributions in many applications, such as statistical mechanics, Bayesianstatistics and generative models. However, such distributions tend to have thinhigh density regions, posing a significant challenge for symplectic integratorsto maintain the small energy errors needed for a high acceptance probability.Instead, we propose a variant called Conservative Hamiltonian Monte Carlo,using $R$--reversible energy-preserving integrators to retain a high acceptanceprobability. We show our algorithm can achieve approximate stationarity with anerror determined by the Jacobian approximation of the energy-preservingproposal map. Numerical evidence shows improved convergence and robustness overintegration parameters on target distributions with thin high density regionsand in high dimensions. Moreover, a version of our algorithm can also beapplied to target distributions without gradient information.

Improving sampling efficacy on high dimensional distributions with thin high density regions using Conservative Hamiltonian Monte Carlo