The p(parameter)-CSMC algorithm is proposed, which incorporates both parameter learning and ancestor sampling, leading to much better mixing properties compared to (particle) Gibbs sampling in settings where strong internal correlations may challenge effective exploration.
Sampling from the posterior is the central task of computational Bayesian inverse problems. The standard workhorse in Bayesian inference - Markov chain Monte Carlo (MCMC) - is sequential, yields correlated samples, and must be rerun for each observation. Conditional flow matching offers an amortized alternative: a tran...
Censoring strategies in reliability testing are often adopted to control testing time, sample size, and cost. To improve inferential efficiency under such settings, this study considers an adaptive Type‐II hybrid progressive censoring scheme. The recently proposed one‐parameter Weighted Exponential–Lindley distributi...
Bayesian computation for survival analysis often involves selecting among alternative Markov chain Monte Carlo (MCMC) algorithms whose performance can vary substantially with the characteristics of the data and model. In practice, this selection is frequently based on trial and error, requiring repeated model fitting a...
The coefficient of variation (CV) is a frequently used standardized measure of variability that allows meaningful comparison among datasets measured using different scales or units. Although widely applied in practice, formal statistical inference for the CV under non-normal distributions has received limited attention...
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