Precision Parameter Estimation and Machine Learning

Computer Science – Databases

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

Monte Carlo Methods, Other Luminescence And Radiative Recombination, Cosmology, Astronomical Catalogs, Atlases, Sky Surveys, Databases, Retrieval Systems, Archives, Etc.

Scientific paper

I discuss the strategy of ``Acceleration by Parallel Precomputation and Learning'' (AP-PLe) that can vastly accelerate parameter estimation in high-dimensional parameter spaces and costly likelihood functions, using trivially parallel computing to speed up sequential exploration of parameter space. This strategy combines the power of distributed computing with machine learning and Markov-Chain Monte Carlo techniques efficiently to explore a likelihood function, posterior distribution or χ2-surface. This strategy is particularly successful in cases where computing the likelihood is costly and the number of parameters is moderate or large. We apply this technique to two central problems in cosmology: the solution of the cosmological parameter estimation problem with sufficient accuracy for the Planck data using PICo; and the detailed calculation of cosmological helium and hydrogen recombination with RICO. Since the APPLe approach is designed to be able to use massively parallel resources to speed up problems that are inherently serial, we can bring the power of distributed computing to bear on parameter estimation problems. We have demonstrated this with the CosmologyatHome project.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Precision Parameter Estimation and Machine Learning does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Precision Parameter Estimation and Machine Learning, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Precision Parameter Estimation and Machine Learning will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-1099958

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.