Fetching the paper…
Reading the bibliography…
The use of heuristics to assess the convergence and compress the output of Markov chain Monte Carlo can be sub-optimal in terms of the empirical approximations that are produced.
Elements of physical biology
A. J. Lotka · 1926
Earlier work this paper cites.
Variazioni e fluttuazioni del numero d’individui in specie animali conviventi
V. Volterra · 1926
Earlier work this paper cites.
Oscillatory behavior in enzymatic control process
B. C. Goodwin · 1965
Earlier work this paper cites.
Monte Carlo sampling methods using Markov chains and their applications
W. K. Hastings · 1970
Earlier work this paper cites.
A bound for the error in the normal approximation to the distribution of a sum of dependent random variables
C. Stein · 1972
Earlier work this paper cites.
Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images
S. Geman and D. Geman · 1984
Earlier work this paper cites.
The calculation of posterior distributions by data augmentation
M. A. Tanner and W. H. Wong · 1987
Earlier work this paper cites.
Sampling-based approaches to calculating marginal densities
A. E. Gelfand and A. F. Smith · 1990
Earlier work this paper cites.
Inference from iterative simulation using multiple sequences
A. Gelman and D. B. Rubin · 1992
Earlier work this paper cites.
Practical Markov chain Monte Carlo
C. J. Geyer · 1992
Earlier work this paper cites.
Constrained Monte Carlo maximum likelihood for dependent data
C. J. Geyer and E. A. Thompson · 1992
Earlier work this paper cites.
Computable bounds for geometric convergence rates of Markov chains
S. Meyn and R. Tweedie · 1994
Earlier work this paper cites.
Minorization conditions and convergence rates for Markov chain Monte Carlo
J. S. Rosenthal · 1995
Earlier work this paper cites.
Markov chain Monte Carlo convergence diagnostics: A comparative review
M. K. Cowles and B. P. Carlin · 1996
Earlier work this paper cites.
Exponential convergence of Langevin distributions and their discrete approximations
G. O. Roberts and R. L. Tweedie · 1996
Earlier work this paper cites.
Weak convergence and optimal scaling of random walk Metropolis algorithms
A. Gelman, W. R. Gilks, and G. O. Roberts · 1997
Earlier work this paper cites.
Integral probability metrics and their generating classes of functions
A. Muller · 1997
Earlier work this paper cites.
General methods for monitoring convergence of iterative simulations
S. P. Brooks and A. Gelman · 1998
Earlier work this paper cites.
A generalized discrepancy and quadrature error bound
F. Hickernell · 1998
Earlier work this paper cites.
Adaptive proposal distribution for random walk Metropolis algorithm
H. Haario, E. Saksman, and J. Tamminen · 1999
Earlier work this paper cites.
Bounds on regeneration times and convergence rates for Markov chains
G. O. Roberts and R. L. Tweedie · 1999
Earlier work this paper cites.
WinBUGS - a Bayesian modelling framework: Concepts, structure, and extensibility
D. J. Lunn, A. Thomas, N. Best, and D. Spiegelhalter · 2000
Earlier work this paper cites.
Honest exploration of intractable probability distributions via Markov chain Monte Carlo
G. L. Jones and J. P. Hobert · 2001
Earlier work this paper cites.
Optimal scaling for various Metropolis–Hastings algorithms
G. O. Roberts and J. S. Rosenthal · 2001
Earlier work this paper cites.
JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling
M. Plummer · 2003
Earlier work this paper cites.
On a new multivariate two-sample test
L. Baringhaus and C. Franz · 2004
Earlier work this paper cites.
Reproducing Kernel Hilbert Spaces in Probability and Statistics
A. Berlinet and C. Thomas-Agnan · 2004
Earlier work this paper cites.
A simplified local control model of calcium-induced calcium release in cardiac ventricular myocytes
R. Hinch, J. Greenstein, A. Tanskanen, L. Xu, and R. Winslow · 2004
Earlier work this paper cites.
General state space Markov chains and MCMC algorithms
G. O. Roberts and J. S. Rosenthal · 2004
Cited alongside, same era.
Testing for equal distributions in high dimension
G. J. Székely and M. L. Rizzo · 2004
Cited alongside, same era.
SUNDIALS: Suite of nonlinear and differential/algebraic equation solvers
A. C. Hindmarsh, P. N. Brown, K. E. Grant, S. L. Lee, R. Serban, D. E. Shumaker, and C. S. Woodward · 2005
Cited alongside, same era.
CODA: Convergence diagnosis and output analysis for MCMC
M. Plummer, N. Best, K. Cowles, and K. Vines · 2006
Cited alongside, same era.
Markov chain Monte Carlo: Can we trust the third significant figure?
J. M. Flegal, M. Haran, and G. L. Jones · 2008
Cited alongside, same era.
Learning via Hilbert space embedding of distributions
L. Song · 2008
Cited alongside, same era.
Stein points
W. Y. Chen, L. Mackey, J. Gorham, F.-X. Briol, and C. J. Oates · 2018
Later among the works it cites.
Large sample analysis of the median heuristic
D. Garreau, W. Jitkrittum, and M. Kanagawa · 2018
Later among the works it cites.
Random feature Stein discrepancies
J. Huggins and L. Mackey · 2018
Later among the works it cites.
Riemannian Stein variational gradient descent for Bayesian inference
C. Liu and J. Zhu · 2018
Later among the works it cites.
Support points
S. Mak and V. R. Joseph · 2018
Later among the works it cites.
Lugsail lag windows and their application to MCMC
D. Vats and J. M. Flegal · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Estimating Bayes factors via thermodynamic integration and population MCMC
B. Calderhead and M. Girolami · 2009
Cited alongside, same era.
Hilbert space embeddings of conditional distributions with applications to dynamical systems
L. Song, J. Huang, A. Smola, and K. Fukumizu · 2009
Cited alongside, same era.
Riemann manifold Langevin and Hamiltonian Monte Carlo methods
M. Girolami and B. Calderhead · 2011
Cited alongside, same era.
Simulating human cardiac electrophysiology on clinical time-scales
S. A. Niederer, L. Mitchell, N. Smith, and G. Plank · 2011
Cited alongside, same era.
Markov Chains and Stochastic Stability
S. Meyn and R. Tweedie · 2012
Cited alongside, same era.
Monte Carlo Statistical Methods
C. Robert and G. Casella · 2013
Cited alongside, same era.
Revisiting the Gelman–Rubin diagnostic
D. Vats and C. Knudson · 2018
Later among the works it cites.
Estimating convergence of Markov chains with L-lag couplings
N. Biswas, P. E. Jacob, and P. Vanetti · 2019
Later among the works it cites.
Stein points Markov chain Monte Carlo
W. Y. Chen, A. Barp, F.-X. Briol, J. Gorham, L. Mackey, M. Girolami, and C. J. Oates · 2019
Later among the works it cites.
Arrhythmia mechanisms and spontaneous calcium release: Bi-directional coupling between re-entrant and focal excitation
M. A. Colman · 2019
Later among the works it cites.
On the geometry of Stein variational gradient descent
A. Duncan, N. Nüsken, and L. Szpruch · 2019
Later among the works it cites.
The power of online thinning in reducing discrepancy
R. Dwivedi, O. N. Feldheim, O. Gurel-Gurevich, and A. Ramdas · 2019
Later among the works it cites.
Deterministic sampling of expensive posteriors using minimum energy designs
V. R. Joseph, D. Wang, L. Gu, S. Lyu, and R. Tuo · 2019
Later among the works it cites.
Computational models in cardiology
S. A. Niederer, J. Lumens, and N. A. Trayanova · 2019
Later among the works it cites.
Multivariate output analysis for Markov chain Monte Carlo
D. Vats, J. M. Flegal, and G. L. Jones · 2019
Later among the works it cites.
Stochastic Stein discrepancies
J. Gorham, A. Raj, and L. Mackey · 2020
Closest in time.
The reproducing Stein kernel approach for post-hoc corrected sampling
L. Hodgkinson, R. Salomone, and F. Roosta · 2020
Closest in time.
stableGR , 2020
C. Knudson and D. Vats · 2020
Closest in time.
A diffusion approach to Stein’s method on Riemannian manifolds
H. Le, A. Lewis, K. Bharath, and C. Fallaize · 2020
Closest in time.
The barker proposal: combining robustness and efficiency in gradient-based mcmc
S. Livingstone and G. Zanella · 2020
Closest in time.
R: A Language and Environment for Statistical Computing
R Core Team · 2020
Closest in time.
Simulating ventricular systolic motion in a four-chamber heart model with spatially varying robin boundary conditions to model the effect of the pericardium
M. Strocchi, M. A. Gsell, C. M. Augustin, O. Razeghi, C. H. Roney, A. J. Prassl, E. J. Vigmond, J. M. Behar, J. S. Gould, C. A. Rinaldi, M. J. Bishop, G. Plank, and S. A. Niederer · 2020
Closest in time.
A Stein goodness-of-fit test for directional distributions
W. Xu and T. Matsuda · 2020
Closest in time.
A Riemann–Stein kernel method
A. Barp, C. Oates, E. Porcu, and M. Girolami · 2021
Closest in time.
Robust generalised Bayesian inference for intractable likelihoods
T. Matsubara, J. Knoblauch, F.-X. Briol, and C. J. Oates · 2021
Closest in time.
Semi-exact control functionals from Sard’s method
L. F. South, T. Karvonen, C. Nemeth, M. Girolami, and C. Oates · 2021
Closest in time.
Optimal quantisation of probability measures using maximum mean discrepancy
O. Teymur, J. Gorham, M. Riabiz, and C. J. Oates · 2021
Closest in time.