ResearchTrend.AI
Precomputing Strategy for Hamiltonian Monte Carlo Method Based on Regularity in Parameter Space

Precomputing Strategy for Hamiltonian Monte Carlo Method Based on Regularity in Parameter Space

Cheng Zhang
Babak Shahbaba
Hongkai Zhao
ArXivPDFHTML

Papers citing "Precomputing Strategy for Hamiltonian Monte Carlo Method Based on Regularity in Parameter Space"

7 / 7 papers shown
Title
Stochastic Gradient Hamiltonian Monte Carlo
Stochastic Gradient Hamiltonian Monte Carlo
Tianqi Chen
E. Fox
Carlos Guestrin
BDL
50
902
0
17 Feb 2014
Accelerating Asymptotically Exact MCMC for Computationally Intensive Models via Local Approximations
Accelerating Asymptotically Exact MCMC for Computationally Intensive Models via Local Approximations
Patrick R. Conrad
Youssef M. Marzouk
Natesh S. Pillai
Aaron Smith
47
116
0
07 Feb 2014
Spherical Hamiltonian Monte Carlo for Constrained Target Distributions
Spherical Hamiltonian Monte Carlo for Constrained Target Distributions
Shiwei Lan
Bo Zhou
Babak Shahbaba
59
58
0
17 Sep 2013
MCMC using Hamiltonian dynamics
MCMC using Hamiltonian dynamics
Radford M. Neal
280
3,278
0
09 Jun 2012
The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo
The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo
Matthew D. Hoffman
Andrew Gelman
118
4,275
0
18 Nov 2011
Split Hamiltonian Monte Carlo
Split Hamiltonian Monte Carlo
Babak Shahbaba
Shiwei Lan
W. Johnson
Radford M. Neal
124
92
0
29 Jun 2011
Uncertainty quantification and weak approximation of an elliptic inverse problem
Uncertainty quantification and weak approximation of an elliptic inverse problem
Masoumeh Dashti
Andrew M. Stuart
36
101
0
01 Feb 2011