Deep Neural Network Informed Markov Chain Monte Carlo Methods
- Portland State Univ., OR (United States)
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
In subsurface flow modeling, quantifying the uncertainty of model parameters and the corresponding uncertainly on output quantities is a crucial task for groundwater management. Markov chain Monte Carlo (MCMC) methods can take advantage of observed data to estimate parameters in a Bayesian setting. However, MCMC can be slow to converge and produce highly correlated samples when the dimensions of the parameters is high. Using gradients for the posterior distribution can help samplers explore the parameter space more efficiently, but obtaining gradients can be computationally challenging.
- Research Organization:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA)
- DOE Contract Number:
- AC52-07NA27344
- OSTI ID:
- 2283285
- Report Number(s):
- LLNL--TR-857609; 1087734
- Country of Publication:
- United States
- Language:
- English
Similar Records
Challenges in Markov Chain Monte Carlo for Bayesian Neural Networks
Laplacian Smoothing Stochastic Gradient Markov Chain Monte Carlo
MARKOV CHAIN MONTE CARLO POSTERIOR SAMPLING WITH THE HAMILTONIAN METHOD
Journal Article
·
Tue Jun 21 00:00:00 EDT 2022
· Statistical Science
·
OSTI ID:1976073
Laplacian Smoothing Stochastic Gradient Markov Chain Monte Carlo
Journal Article
·
Sun Jan 03 23:00:00 EST 2021
· SIAM Journal on Scientific Computing
·
OSTI ID:1866812
MARKOV CHAIN MONTE CARLO POSTERIOR SAMPLING WITH THE HAMILTONIAN METHOD
Conference
·
Wed Jan 31 23:00:00 EST 2001
·
OSTI ID:775292