The following pages link to Chris Sherlock (Q261047):
Displaying 31 items.
- Delayed acceptance particle MCMC for exact inference in stochastic kinetic models (Q261048) (← links)
- Optimal scaling for the pseudo-marginal random walk Metropolis: insensitivity to the noise generating mechanism (Q340131) (← links)
- Optimal scaling of the random walk Metropolis on elliptically symmetric unimodal targets (Q605022) (← links)
- A class of spherical and elliptical distributions with Gaussian-like limit properties (Q764432) (← links)
- The random walk Metropolis: linking theory and practice through a case study (Q903288) (← links)
- Merging MCMC subposteriors through Gaussian-process approximations (Q1631561) (← links)
- Bayesian inference for diffusion-driven mixed-effects models (Q1699666) (← links)
- Improved bridge constructs for stochastic differential equations (Q1703803) (← links)
- Efficiency of delayed-acceptance random walk metropolis algorithms (Q2054541) (← links)
- Augmented pseudo-marginal Metropolis-Hastings for partially observed diffusion processes (Q2114055) (← links)
- Direct statistical inference for finite Markov jump processes via the matrix exponential (Q2135942) (← links)
- Variance bounding of delayed-acceptance kernels (Q2157432) (← links)
- Motor unit number estimation via sequential Monte Carlo (Q2291281) (← links)
- Efficient sampling of conditioned Markov jump processes (Q2329829) (← links)
- On the efficiency of pseudo-marginal random walk Metropolis algorithms (Q2338926) (← links)
- Bayesian inference for hybrid discrete-continuous stochastic kinetic models (Q2936499) (← links)
- An Exact Gibbs Sampler for the Markov-Modulated Poisson Process (Q3442938) (← links)
- Optimal Scaling of the Random Walk Metropolis: General Criteria for the 0.234 Acceptance Rule (Q4918557) (← links)
- A discrete bouncy particle sampler (Q5081556) (← links)
- Inference for reaction networks using the linear noise approximation (Q5170219) (← links)
- Particle Metropolis-adjusted Langevin algorithms (Q5384403) (← links)
- Pseudo-marginal Metropolis–Hastings sampling using averages of unbiased estimators (Q5384504) (← links)
- Hug and hop: a discrete-time, nonreversible Markov chain Monte Carlo algorithm (Q6045143) (← links)
- Recruitment prediction for multicenter clinical trials based on a hierarchical Poisson–gamma model: Asymptotic analysis and improved intervals (Q6079483) (← links)
- Accelerating inference for stochastic kinetic models (Q6115546) (← links)
- Pseudo-marginal Metropolis--Hastings using averages of unbiased estimators (Q6279223) (← links)
- Scalable couplings for the random walk Metropolis algorithm (Q6507131) (← links)
- SwISS: a scalable Markov chain Monte Carlo divide-and-conquer strategy (Q6548764) (← links)
- Tamás P. Papp, Paul Fearnhead, and Chris Sherlock's contribution to the discussion of `the Discussion Meeting on Probabilistic and statistical aspects of machine learning' (Q6569522) (← links)
- A coupled hidden Markov model for disease interactions (Q6638969) (← links)
- Inference for extreme values under threshold-based stopping rules (Q6642201) (← links)
