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Found 13 papers, 11 papers with code
On the convergence of dynamic implementations of Hamiltonian Monte Carlo and No U-Turn Samplers
Under conditions similar to the ones existing for HMC, we also show that NUTS is geometrically ergodic.
Simulating counterfactuals
Counterfactual inference considers a hypothetical intervention in a parallel world that shares some evidence with the factual world.
bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R
We present an R package bssm for Bayesian non-linear/non-Gaussian state space modelling.
Bayesian Inference
Computation
Conditional particle filters with diffuse initial distributions
The method only requires simulatable Markov transitions that are reversible with respect to the initial distribution, which can be improper.
Computation Methodology
On the use of approximate Bayesian computation Markov chain Monte Carlo with inflated tolerance and post-correction
Approximate Bayesian computation allows for inference of complicated probabilistic models with intractable likelihoods using model simulations.
Computation
Graphical model inference: Sequential Monte Carlo meets deterministic approximations
Approximate inference in probabilistic graphical models (PGMs) can be grouped into deterministic methods and Monte-Carlo-based methods.
Unbiased inference for discretely observed hidden Markov model diffusions
The resulting estimator leads to inference without a bias from the time-discretisation as the number of Markov chain iterations increases.
Bayesian Inference
Methodology
Probability
Computation
65C05 (primary), 60H35, 65C35, 65C40 (secondary)
Coupled conditional backward sampling particle filter
This complements the earlier findings in the literature for conditional particle filters, which assume the number of particles to grow (super)linearly in terms of the time horizon.
Computation Probability Primary 65C05, secondary 60J05, 65C35, 65C40
Importance sampling type estimators based on approximate marginal MCMC
We consider importance sampling (IS) type weighted estimators based on Markov chain Monte Carlo (MCMC) targeting an approximate marginal of the target distribution.
Computation Probability
Unbiased estimators and multilevel Monte Carlo
Under general conditions, essentially when MLMC admits the canonical square root Monte Carlo error rate, the proposed new schemes are shown to be asymptotically as efficient as MLMC, both in terms of variance and cost.
Computation Probability 65C05 (Primary), 65C30 (Secondary)
Adaptive parallel tempering algorithm
Parallel tempering is a generic Markov chain Monte Carlo sampling method which allows good mixing with multimodal target distributions, where conventional Metropolis-Hastings algorithms often fail.
Computation
Robust adaptive Metropolis algorithm with coerced acceptance rate
This paper introduces a new robust adaptive Metropolis algorithm estimating the shape of the target distribution and simultaneously coercing the acceptance rate.
Computation 65C40 (Primary) 60J22, 60J05, 93E35 (Secondary)
Grapham: Graphical Models with Adaptive Random Walk Metropolis Algorithms
Recently developed adaptive Markov chain Monte Carlo (MCMC) methods have been applied successfully to many problems in Bayesian statistics.
Computation
