Nonparametric Bayesian Calibration of Computer Models
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908677380767744 |
|---|---|
| author | Shi, Haiyi Yang, Lei Chi, Jiarui Butler, Troy Wang, Haonan Bingham, Derek Estep, Don |
| author_facet | Shi, Haiyi Yang, Lei Chi, Jiarui Butler, Troy Wang, Haonan Bingham, Derek Estep, Don |
| contents | Calibration of computer models is a key step in making inferences, predictions, and decisions for complex science and engineering systems. We formulate and analyze a nonparametric Bayesian methodology for computer model calibration. This paper presents a number of key results including; establishment of a unique nonparametric Bayesian posterior corresponding to a chosen prior with an explicit formula for the corresponding conditional density; a maximum entropy property of the posterior corresponding to the uniform prior; the almost everywhere continuity of the density of the nonparametric posterior; and a comprehensive convergence and asymptotic analysis of an estimator based on a form of importance sampling. We illustrate the problem and results using several examples, including a simple experiment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_22597 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Nonparametric Bayesian Calibration of Computer Models Shi, Haiyi Yang, Lei Chi, Jiarui Butler, Troy Wang, Haonan Bingham, Derek Estep, Don Methodology Statistics Theory Computation Primary 62G05, 65C60 Secondary 62P30, 62P35, 60D05, 60A10 Calibration of computer models is a key step in making inferences, predictions, and decisions for complex science and engineering systems. We formulate and analyze a nonparametric Bayesian methodology for computer model calibration. This paper presents a number of key results including; establishment of a unique nonparametric Bayesian posterior corresponding to a chosen prior with an explicit formula for the corresponding conditional density; a maximum entropy property of the posterior corresponding to the uniform prior; the almost everywhere continuity of the density of the nonparametric posterior; and a comprehensive convergence and asymptotic analysis of an estimator based on a form of importance sampling. We illustrate the problem and results using several examples, including a simple experiment. |
| title | Nonparametric Bayesian Calibration of Computer Models |
| topic | Methodology Statistics Theory Computation Primary 62G05, 65C60 Secondary 62P30, 62P35, 60D05, 60A10 |
| url | https://arxiv.org/abs/2509.22597 |