This special issue is dedicated to Sir Adrian Smith, whose contributions to Bayesian analysis
have deeply impacted the field (or rather fields) of Bayesian inference, decision theory and …

Simulation-based inference (SBI) techniques are now an essential tool for the parameter
estimation of mechanistic and simulatable models with intractable likelihoods. Statistical …

In recent years, with the decline in marine fishery resources, ecosystem-based fisheries
management (EBFM) has emerged as an important paradigm in fisheries management …

Hierarchical models are common for ecological analysis, but determining appropriate model
selection methods remains an ongoing challenge. To confront this challenge, a suitable …

Simulation based inference (SBI) methods enable the estimation of posterior distributions
when the likelihood function is intractable, but where model simulation is feasible. Popular …

Aim: This study aims to correct undercounts in cancer data before initiating a population-
based cancer registry program, employing an innovative Bayesian methodology …

Data collected from arrays of sensors are essential for informed decision-making in various
systems. However, the presence of anomalies can compromise the accuracy and reliability …

Data is fundamental to good decision making, but data collection is often costly and difficult.
Efficient designs for collecting high-quality, relevant data are therefore essential. This …

Bayesian statistics is a framework in which our knowledge about unknown quantities of
interest (especially parameters) is updated with the information in observed data, though it …

This paper mainly explores the application of Bayesian optimization in router port testing,
aiming to improve the efficiency and accuracy of port scanning by constructing a Bayesian …