TY - GEN
T1 - PROXIMAL-LANGEVIN SAMPLERS FOR NONSMOOTH COMPOSITE POSTERIORS
T2 - 31st European Signal Processing Conference, EUSIPCO 2023
AU - Abry, Patrice
AU - Fort, Gersende
AU - Pascal, Barbara
AU - Pustelnik, Nelly
N1 - Publisher Copyright:
© 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - Providing a level of confidence in the estimation of epidemiological indicators during pandemics is essential to inform decision makers. Monitoring the time evolution of the epidemic intensity despite the limited quality of the data is both crucial and challenging. For the estimation of the Covid-19 reproduction number through credibility intervals, a Bayesian model robust to errors in reported counts were proposed, yielding a non differentiable composite a posteriori log-density which required the design of advanced Proximal Langevin schemes. The first goal of this paper is to customize and compare on a pedagogically designed toy example, four different Hastings-Metropolis algorithms combining Langevin approaches and proximal operators. Then, the most efficient one is plugged into a Metropolis-within-Gibbs algorithm performing a credibility intervals-based estimation of Covid-19 pandemic indicators, exemplified for several countries worldwide.
AB - Providing a level of confidence in the estimation of epidemiological indicators during pandemics is essential to inform decision makers. Monitoring the time evolution of the epidemic intensity despite the limited quality of the data is both crucial and challenging. For the estimation of the Covid-19 reproduction number through credibility intervals, a Bayesian model robust to errors in reported counts were proposed, yielding a non differentiable composite a posteriori log-density which required the design of advanced Proximal Langevin schemes. The first goal of this paper is to customize and compare on a pedagogically designed toy example, four different Hastings-Metropolis algorithms combining Langevin approaches and proximal operators. Then, the most efficient one is plugged into a Metropolis-within-Gibbs algorithm performing a credibility intervals-based estimation of Covid-19 pandemic indicators, exemplified for several countries worldwide.
KW - Bayesian credibility intervals
KW - Covid-19
KW - Langevin Monte Carlo
KW - Markov chain Monte Carlo
KW - Proximal operators
KW - Reproduction number
KW - log-concave composite density
U2 - 10.23919/EUSIPCO58844.2023.10290048
DO - 10.23919/EUSIPCO58844.2023.10290048
M3 - Conference contribution
AN - SCOPUS:85178350817
T3 - European Signal Processing Conference
SP - 1813
EP - 1817
BT - 31st European Signal Processing Conference, EUSIPCO 2023 - Proceedings
PB - European Signal Processing Conference, EUSIPCO
Y2 - 4 September 2023 through 8 September 2023
ER -