Matches in SemOpenAlex for { <https://semopenalex.org/work/W3154256989> ?p ?o ?g. }
Showing items 1 to 62 of
62
with 100 items per page.
- W3154256989 abstract "A ubiquitous problem in physics is to determine expectation values of observables associated with a system. This problem is typically formulated as an integration of some likelihood over a multidimensional parameter space. In Bayesian analysis, numerical Markov Chain Monte Carlo (MCMC) algorithms are employed to solve such integrals using a fixed number of samples in the Markov Chain. In general, MCMC algorithms are computationally expensive for large datasets and have difficulties sampling from multimodal parameter spaces. An MCMC implementation that is robust and inexpensive for researchers is desired. Distributed computing systems have shown the potential to act as virtual supercomputers, such as in the SETI@home project in which millions of private computers participate. We propose that a clustered peer-to-peer (P2P) computer network serves as an ideal structure to run Markovian state exchange algorithms such as Parallel Tempering (PT). PT overcomes the difficulty in sampling from multimodal distributions by running multiple chains in parallel with different target distributions andexchanging their states in a Markovian manner. To demonstrate the feasibility of peer-to-peer Parallel Tempering (P2P PT), a simple two-dimensional dataset consisting of two Gaussian signals separated by a region of low probability was used in a Bayesian parameter fitting algorithm. A small connected peer-to-peer network was constructed using separate processes on a linux kernel, and P2P PT was applied to the dataset. These sampling results were compared with those obtained from sampling the parameter space with a single chain. It was found that the single chain was unable to sample both modes effectively, while the P2P PT method explored the target distribution well, visiting both modes approximately equally. Future work will involve scaling to many dimensions and large networks, and convergence conditions with highly heterogeneous computing capabilities of members within the network." @default.
- W3154256989 created "2021-04-26" @default.
- W3154256989 creator A5084877222 @default.
- W3154256989 date "2018-02-20" @default.
- W3154256989 modified "2023-09-27" @default.
- W3154256989 title "A Distributed Computer System for Parallel Markov Chain Monte Carlo (MCMC)" @default.
- W3154256989 doi "https://doi.org/10.24908/iqurcp.9597" @default.
- W3154256989 hasPublicationYear "2018" @default.
- W3154256989 type Work @default.
- W3154256989 sameAs 3154256989 @default.
- W3154256989 citedByCount "0" @default.
- W3154256989 crossrefType "journal-article" @default.
- W3154256989 hasAuthorship W3154256989A5084877222 @default.
- W3154256989 hasConcept C105795698 @default.
- W3154256989 hasConcept C107673813 @default.
- W3154256989 hasConcept C111350023 @default.
- W3154256989 hasConcept C119857082 @default.
- W3154256989 hasConcept C13153151 @default.
- W3154256989 hasConcept C154945302 @default.
- W3154256989 hasConcept C187653413 @default.
- W3154256989 hasConcept C19499675 @default.
- W3154256989 hasConcept C33923547 @default.
- W3154256989 hasConcept C41008148 @default.
- W3154256989 hasConcept C98763669 @default.
- W3154256989 hasConceptScore W3154256989C105795698 @default.
- W3154256989 hasConceptScore W3154256989C107673813 @default.
- W3154256989 hasConceptScore W3154256989C111350023 @default.
- W3154256989 hasConceptScore W3154256989C119857082 @default.
- W3154256989 hasConceptScore W3154256989C13153151 @default.
- W3154256989 hasConceptScore W3154256989C154945302 @default.
- W3154256989 hasConceptScore W3154256989C187653413 @default.
- W3154256989 hasConceptScore W3154256989C19499675 @default.
- W3154256989 hasConceptScore W3154256989C33923547 @default.
- W3154256989 hasConceptScore W3154256989C41008148 @default.
- W3154256989 hasConceptScore W3154256989C98763669 @default.
- W3154256989 hasLocation W31542569891 @default.
- W3154256989 hasOpenAccess W3154256989 @default.
- W3154256989 hasPrimaryLocation W31542569891 @default.
- W3154256989 hasRelatedWork W1490386869 @default.
- W3154256989 hasRelatedWork W1502464768 @default.
- W3154256989 hasRelatedWork W1523738510 @default.
- W3154256989 hasRelatedWork W1567199881 @default.
- W3154256989 hasRelatedWork W1602716952 @default.
- W3154256989 hasRelatedWork W169070267 @default.
- W3154256989 hasRelatedWork W2007883828 @default.
- W3154256989 hasRelatedWork W2039543594 @default.
- W3154256989 hasRelatedWork W2064284814 @default.
- W3154256989 hasRelatedWork W2084457753 @default.
- W3154256989 hasRelatedWork W2325939645 @default.
- W3154256989 hasRelatedWork W2953805832 @default.
- W3154256989 hasRelatedWork W2999695895 @default.
- W3154256989 hasRelatedWork W3006476325 @default.
- W3154256989 hasRelatedWork W3147209356 @default.
- W3154256989 hasRelatedWork W3184179840 @default.
- W3154256989 hasRelatedWork W4120291 @default.
- W3154256989 hasRelatedWork W2278142856 @default.
- W3154256989 hasRelatedWork W2887127167 @default.
- W3154256989 hasRelatedWork W2933961945 @default.
- W3154256989 isParatext "false" @default.
- W3154256989 isRetracted "false" @default.
- W3154256989 magId "3154256989" @default.
- W3154256989 workType "article" @default.