R scripts for Hamiltonian Monte Carlo and Coalescence Towards Perfect Simulation of General Continuous Distributions
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'R scripts to accompany the publication listed below.The methodology and results demonstrate for the first time the feasibility of coalescence and near-perfect simulation for fairly general continuous distributions. This constitutes a quantum leap in MCMC convergence testing by decoupling the convergence error from experimental error and providing, for a given set of starting points, an exact number of iterations in which coalescence occurs' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Bayesian inference","Bayesian Lasso","Coupling from the past","Full Random Uniform Trajectory Sampler","Hamiltonian Monte Carlo","Hybrid Monte Carlo","Markov chain Monte Carlo","MCMC convergence diagnostics","Perfect simulation","Read-once coupling from the past"]} <|eot_id|>
 Here is the Description of a dataset. Description:'R scripts to accompany the publication listed below.The methodology and results demonstrate for the first time the feasibility of coalescence and near-perfect simulation for fairly general continuous distributions. This constitutes a quantum leap in MCMC convergence testing by decoupling the convergence error from experimental error and providing, for a given set of starting points, an exact number of iterations in which coalescence occurs' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
R scripts to accompany the publication listed below.The methodology and results demonstrate for the first time the feasibility of coalescence and near-perfect simulation for fairly general continuous distributions. This constitutes a quantum leap in MCMC convergence testing by decoupling the convergence error from experimental error and providing, for a given set of starting points, an exact number of iterations in which coalescence occurs
Bayesian inference; Bayesian Lasso; Coupling from the past; Full Random Uniform Trajectory Sampler; Hamiltonian Monte Carlo; Hybrid Monte Carlo; Markov chain Monte Carlo; MCMC convergence diagnostics; Perfect simulation; Read-once coupling from the past