The Learning Rate Is Not a Constant: Sandwich-Adjusted Markov Chain Monte Carlo Simulation. [PDF]
Vrugt JA, Diks CGH.
europepmc +1 more source
Correction to ‘An evaluation of multi‐species occupancy models with correlated species occurrences’
Methods in Ecology and Evolution, EarlyView.
wiley +1 more source
The Impact of Risk Exposure and Environmental Conditions on European Banking Efficiency
ABSTRACT This study investigates the determinants of bank cost efficiency in Europe by applying a dynamic Bayesian stochastic frontier approach. We distinguish between two conceptually and empirically separate components of efficiency: (1) an intrinsic component, driven by internal bank features such as size and risk exposure, and (2) an extrThis study
Pilar Gargallo +2 more
wiley +1 more source
Parameter-expanded data augmentation for analyzing multinomial probit models. [PDF]
Zhang X.
europepmc +1 more source
ABSTRACT Rebuilding fish stocks to levels above which they produce Maximum Sustainable Yield (MSY) is a management aim for all European commercially exploited stocks. Progress is typically monitored against the fishing mortality that produces MSY in the long term (FMSY), however, the corresponding biomass target (BMSY) is rarely evaluated nor reported.
Henning Winker +5 more
wiley +1 more source
BayesianFitForecast: a user-friendly R toolbox for parameter estimation and forecasting with ordinary differential equations. [PDF]
Karami H +3 more
europepmc +1 more source
When in Doubt, Tax More Progressively? Uncertainty and Progressive Income Taxation
ABSTRACT We study the optimal income tax problem under parameter uncertainty about household preferences and wage dynamics. We derive conditions characterizing how such uncertainty affects optimal tax policy. To quantify the effect, we estimate a life‐cycle model using US data and a Bayesian approach.
Minsu Chang, Chunzan Wu
wiley +1 more source
Regional heterogeneity in left atrial stiffness impacts passive deformation in a cohort of patient-specific models. [PDF]
Baptiste TMG +15 more
europepmc +1 more source
Variance Matrix Priors for Dirichlet Process Mixture Models With Gaussian Kernels
Summary Bayesian mixture modelling is widely used for density estimation and clustering. The Dirichlet process mixture model (DPMM) is the most popular Bayesian non‐parametric mixture modelling approach. In this manuscript, we study the choice of prior for the variance or precision matrix when Gaussian kernels are adopted.
Wei Jing +2 more
wiley +1 more source

