The Bayesian simulation study (BASIS) framework for simulation studies in statistical and methodological research

Cites Work

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Optimization by Simulated Annealing
Sparsity information and regularization in the horseshoe and other shrinkage priors
Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
Markov chain Monte Carlo: can we trust the third significant figure?
Statistical decision theory and Bayesian analysis. 2nd ed
Inference from iterative simulation using multiple sequences
Bayesian statistics and the efficiency and ethics of clinical trials
Analysis of type I and II error rates of Bayesian and frequentist parametric and nonparametric two-sample hypothesis tests under preliminary assessment of normality
Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations
Julia: A Fresh Approach to Numerical Computing
Learn From Thy Neighbor: Parallel-Chain and Regional Adaptive MCMC
Bayesian Thinking in Biostatistics
On the necessity and design of studies comparing statistical methods
Simulation Run Length Control in the Presence of an Initial Transient
Robust meta‐analytic‐predictive priors in clinical trials with historical control information
Bayesian Adaptive Methods for Clinical Trials
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
Asymptotic Statistics
Real-Parameter Evolutionary Monte Carlo With Applications to Bayesian Mixture Models
Sequential Monte Carlo Methods for Dynamic Systems
Particle Markov Chain Monte Carlo Methods
Gaussian Markov Random Fields
Bayesian Methods in Pharmaceutical Research
Bayesian Inference with INLA
On the Assessment of Monte Carlo Error in Simulation-Based Statistical Analyses
Equation of State Calculations by Fast Computing Machines
Simplified integrated nested Laplace approximation
Handbook of Approximate Bayesian Computation
The Bayesian Choice
Introducing Monte Carlo Methods with R
Moving to a World Beyond “p < 0.05”
The ASA Statement on p-Values: Context, Process, and Purpose
Measure and integration theory. Transl. from the German by Robert B. Burckel
An adaptive Metropolis algorithm
Using simulation studies to evaluate statistical methods

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