A binned likelihood for stochastic models
Abstract Metrics of model goodness-of-fit, model comparison, and model parameter estimation are the main categories of statistical problems in science. Bayesian and frequentist methods that address these questions often rely on a likelihood function, which is th...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer Berlin Heidelberg
2021
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| Online Access: | https://hdl.handle.net/1721.1/131681 |
| _version_ | 1811077776378167296 |
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| author | Argüelles, C. A Schneider, A. Yuan, T. |
| author_facet | Argüelles, C. A Schneider, A. Yuan, T. |
| author_sort | Argüelles, C. A |
| collection | MIT |
| description | Abstract
Metrics of model goodness-of-fit, model comparison, and model parameter estimation are the main categories of statistical problems in science. Bayesian and frequentist methods that address these questions often rely on a likelihood function, which is the key ingredient in order to assess the plausibility of model parameters given observed data. In some complex systems or experimental setups, predicting the outcome of a model cannot be done analytically, and Monte Carlo techniques are used. In this paper, we present a new analytic likelihood that takes into account Monte Carlo uncertainties, appropriate for use in the large and small sample size limits. Our formulation performs better than semi-analytic methods, prevents strong claims on biased statements, and provides improved coverage properties compared to available methods. |
| first_indexed | 2024-09-23T10:48:12Z |
| format | Article |
| id | mit-1721.1/131681 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T10:48:12Z |
| publishDate | 2021 |
| publisher | Springer Berlin Heidelberg |
| record_format | dspace |
| spelling | mit-1721.1/1316812021-09-21T03:47:24Z A binned likelihood for stochastic models Argüelles, C. A Schneider, A. Yuan, T. Abstract Metrics of model goodness-of-fit, model comparison, and model parameter estimation are the main categories of statistical problems in science. Bayesian and frequentist methods that address these questions often rely on a likelihood function, which is the key ingredient in order to assess the plausibility of model parameters given observed data. In some complex systems or experimental setups, predicting the outcome of a model cannot be done analytically, and Monte Carlo techniques are used. In this paper, we present a new analytic likelihood that takes into account Monte Carlo uncertainties, appropriate for use in the large and small sample size limits. Our formulation performs better than semi-analytic methods, prevents strong claims on biased statements, and provides improved coverage properties compared to available methods. 2021-09-20T17:29:36Z 2021-09-20T17:29:36Z 2019-06-10 2020-06-26T13:01:43Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/131681 Journal of High Energy Physics. 2019 Jun 10;2019(6):30 PUBLISHER_CC en https://doi.org/10.1007/JHEP06(2019)030 Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ The Author(s) application/pdf Springer Berlin Heidelberg Springer Berlin Heidelberg |
| spellingShingle | Argüelles, C. A Schneider, A. Yuan, T. A binned likelihood for stochastic models |
| title | A binned likelihood for stochastic models |
| title_full | A binned likelihood for stochastic models |
| title_fullStr | A binned likelihood for stochastic models |
| title_full_unstemmed | A binned likelihood for stochastic models |
| title_short | A binned likelihood for stochastic models |
| title_sort | binned likelihood for stochastic models |
| url | https://hdl.handle.net/1721.1/131681 |
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