Zobrazeno 1 - 10
of 123
pro vyhledávání: '"Ajay Jasra"'
Publikováno v:
New Journal of Physics, Vol 22, Iss 6, p 063038 (2020)
Bayesian inference is a powerful paradigm for quantum state tomography, treating uncertainty in meaningful and informative ways. Yet the numerical challenges associated with sampling from complex probability distributions hampers Bayesian tomography
Externí odkaz:
https://doaj.org/article/e9533fed90ee41c99d80218bee8f4452
Publikováno v:
SIAM/ASA Journal on Uncertainty Quantification. 10:584-618
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a61f06dff4b467746e81cdf1d3eee484
https://doi.org/10.1137/21m1423762
https://doi.org/10.1137/21m1423762
Publikováno v:
Advances in Applied Probability. 54:661-687
In this article we consider a Monte-Carlo-based method to filter partially observed diffusions observed at regular and discrete times. Given access only to Euler discretizations of the diffusion process, we present a new procedure which can return on
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::69ed005de30b60f9d3b5e55068eae2f4
https://doi.org/10.1017/apr.2021.50
https://doi.org/10.1017/apr.2021.50
Autor:
Marco Ballesio, Ajay Jasra
Publikováno v:
Monte Carlo Methods and Applications. 28:61-83
In this paper, we consider static parameter estimation for a class of continuous-time state-space models. Our goal is to obtain an unbiased estimate of the gradient of the log-likelihood (score function), which is an estimate that is unbiased even if
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e6fba690de8d0a00528e3a62442f4513
https://doi.org/10.1515/mcma-2022-2105
https://doi.org/10.1515/mcma-2022-2105
Publikováno v:
International Journal for Uncertainty Quantification.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::49973524a1da356a7ced3de9e99df018
https://doi.org/10.1615/int.j.uncertaintyquantification.2023045369
https://doi.org/10.1615/int.j.uncertaintyquantification.2023045369
Publikováno v:
Statistics and Computing. 33
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8cd55b64fa87e727db94e953bf40e2d4
https://doi.org/10.1007/s11222-022-10193-0
https://doi.org/10.1007/s11222-022-10193-0
Publikováno v:
Paulin, D, Jasra, A, Beskos, A & Crisan, D 2022, ' A 4D-Var Method with Flow-Dependent Background Covariances for the Shallow-Water Equations ', Statistics and Computing, vol. 32, 65 . https://doi.org/10.1007/s11222-022-10119-w
The 4D-Var method for filtering partially observed nonlinear chaotic dynamical systems consists of finding the maximum a-posteriori (MAP) estimator of the initial condition of the system given observations over a time window, and propagating it forwa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b81f788d4b395692062ed3fb6900f363
https://doi.org/10.1007/s11222-022-10119-w
https://doi.org/10.1007/s11222-022-10119-w
Publikováno v:
SIAM/ASA Journal on Uncertainty Quantification. 9:763-787
We develop a Bayesian inference method for diffusions observed discretely and with noise, which is free of discretisation bias. Unlike existing unbiased inference methods, our method does not rely on exact simulation techniques. Instead, our method u
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::573083c4346b0db04ee8be09f4986d72
https://doi.org/10.1137/20m131549x
https://doi.org/10.1137/20m131549x
Publikováno v:
Statistics and Computing. 32
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5123be1074c89dfcf66ed2cb27a3109c
https://doi.org/10.1007/s11222-022-10094-2
https://doi.org/10.1007/s11222-022-10094-2
Publikováno v:
Statistics and Computing. 32
Markov chain Monte Carlo (MCMC) is a powerful methodology for the approximation of posterior distributions. However, the iterative nature of MCMC does not naturally facilitate its use with modern highly parallel computation on HPC and cloud environme
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c1607f20d2a44488db6cf5e6ca144564
https://doi.org/10.1007/s11222-022-10093-3
https://doi.org/10.1007/s11222-022-10093-3