scholarly journals Convergence rates for Bayesian estimation and testing in monotone regression

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Moumita Chakraborty ◽  
Subhashis Ghosal
Keyword(s):  
Bayesian Estimation ◽  
Convergence Rates ◽  
Monotone Regression

scholarly journals Multilevel higher-order quasi-Monte Carlo Bayesian estimation

2017 ◽  
Vol 27 (05) ◽  
pp. 953-995 ◽  
Author(s):  
Josef Dick ◽  
Robert N. Gantner ◽  
Quoc T. Le Gia ◽  
Christoph Schwab
Keyword(s):  
Monte Carlo ◽  
Bayesian Estimation ◽  
Convergence Rates ◽  
Applied Mathematics ◽  
Higher Order ◽  
Forward Problem ◽  
Monte Carlo Integration ◽  
High Dimensional ◽  
Operator Equations ◽  
Quasi Monte Carlo

We propose and analyze deterministic multilevel (ML) approximations for Bayesian inversion of operator equations with uncertain distributed parameters, subject to additive Gaussian measurement data. The algorithms use a ML approach based on deterministic, higher-order quasi-Monte Carlo (HoQMC) quadrature for approximating the high-dimensional expectations, which arise in the Bayesian estimators, and a Petrov–Galerkin (PG) method for approximating the solution to the underlying partial differential equation (PDE). This extends the previous single-level (SL) approach from [J. Dick, R. N. Gantner, Q. T. Le Gia and Ch. Schwab, Higher order quasi-Monte Carlo integration for Bayesian estimation, Report 2016-13, Seminar for Applied Mathematics, ETH Zürich (in review)]. Compared to the SL approach, the present convergence analysis of the ML method requires stronger assumptions on holomorphy and regularity of the countably-parametric uncertainty-to-observation maps of the forward problem. As in the SL case and in the affine-parametric case analyzed in [J. Dick, F. Y. Kuo, Q. T. Le Gia and Ch. Schwab, Multi-level higher order QMC Galerkin discretization for affine parametric operator equations, SIAM J. Numer. Anal. 54 (2016) 2541–2568], we obtain sufficient conditions which allow us to achieve arbitrarily high, algebraic convergence rates in terms of work, which are independent of the dimension of the parameter space. The convergence rates are limited only by the spatial regularity of the forward problem, the discretization order achieved by the PG discretization, and by the sparsity of the uncertainty parametrization. We provide detailed numerical experiments for linear elliptic problems in two space dimensions, with [Formula: see text] parameters characterizing the uncertain input, confirming the theory and showing that the ML HoQMC algorithms can outperform, in terms of error versus computational work, both multilevel Monte Carlo (MLMC) methods and SL HoQMC methods, provided the parametric solution maps of the forward problems afford sufficient smoothness and sparsity of the high-dimensional parameter spaces.

BAYESIAN ESTIMATION OF A TIME DEPENDENT INTERNAL HEAT SOURCE IN A HETEROGENEOUS MEDIA

2016 ◽  
Author(s):  
Carolina Palma Naveira Cotta ◽  
Kelvin Chen ◽  
Christopher Tostado ◽  
Philippe Rollemberg d'Egmont ◽  
Fernando Duda ◽  
...  
Keyword(s):  
Heat Source ◽  
Bayesian Estimation ◽  
Heterogeneous Media ◽  
Internal Heat ◽  
Internal Heat Source ◽  
Time Dependent

Stability Analysis of the Nanjung Water Diversion Twin Tunnels based on Convergence Measurement

2019 ◽  
Vol 1 (1) ◽  
pp. 49-60
Author(s):  
Simon Heru Prassetyo ◽  
Ganda Marihot Simangunsong ◽  
Ridho Kresna Wattimena ◽  
Made Astawa Rai ◽  
Irwandy Arif ◽  
...  
Keyword(s):  
Stability Analysis ◽  
Convergence Rate ◽  
Critical Strain ◽  
Convergence Rates ◽  
Strain Analysis ◽  
Water Diversion ◽  
Tunnel Face ◽  
Tunnel Stability ◽  
The Stability ◽  
Twin Tunnels

This paper focuses on the stability analysis of the Nanjung Water Diversion Twin Tunnels using convergence measurement. The Nanjung Tunnel is horseshoe-shaped in cross-section, 10.2 m x 9.2 m in dimension, and 230 m in length. The location of the tunnel is in Curug Jompong, Margaasih Subdistrict, Bandung. Convergence monitoring was done for 144 days between February 18 and July 11, 2019. The results of the convergence measurement were recorded and plotted into the curves of convergence vs. day and convergence vs. distance from tunnel face. From these plots, the continuity of the convergence and the convergence rate in the tunnel roof and wall were then analyzed. The convergence rates from each tunnel were also compared to empirical values to determine the level of tunnel stability. In general, the trend of convergence rate shows that the Nanjung Tunnel is stable without any indication of instability. Although there was a spike in the convergence rate at several STA in the measured span, that spike was not replicated by the convergence rate in the other measured spans and it was not continuous. The stability of the Nanjung Tunnel is also confirmed from the critical strain analysis, in which most of the STA measured have strain magnitudes located below the critical strain line and are less than 1%.

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