About
A full-fledged Bayesian computation requries evaluation of the posterior probability density in the complete parameter space. This can become very time consuming using commonly used algorithms such as Markov Chain Monte Carlos. We present ideas on the parallelization of the Markov Chain Monte Carlo approach via multi-proposal generation and via parameter space partitioning. For the former approach, recent developments in weighted sample generation are described and initial results presented. For massive parallelization via parameter space partitioning, the calculation of the marginal likelihood (evidence) is necessary and we solve this task with the Adaptive Harmonic Mean Integration (AHMI) algorithm. We describe the algorithm and it's mathematical properties, and report the results using it on multiple test cases.
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The talk
Credits and licence
- Institution
- CERN
- Department
- EP-IT Data science seminars
- Speaker
- Allen Caldwell
- Year
- 2020
- Language
- English
- Subjects
- Computer science
- Source
- CERN
- Licence
- CERN, as CERN states it. CERN's terms
- Attribution
- © CERN. CERN provides its audiovisual media free of charge for educational and informational use, credited to CERN; this lecture is shown in CERN's own player.
- Who can watch
- CERN gives no rating. Invisible Network shows it to Tweens profiles and older, judged from what the source says about it. How courses are placed