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A special issue on Bayesian inference: challenges, perspectives and prospects
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 …
have deeply impacted the field (or rather fields) of Bayesian inference, decision theory and …
Misspecification-robust Sequential Neural Likelihood for Simulation-based Inference
Simulation-based inference techniques are indispensable for parameter estimation of
mechanistic and simulable models with intractable likelihoods. While traditional statistical …
mechanistic and simulable models with intractable likelihoods. While traditional statistical …
Preconditioned neural posterior estimation for likelihood-free inference
Simulation based inference (SBI) methods enable the estimation of posterior distributions
when the likelihood function is intractable, but where model simulation is feasible. Popular …
when the likelihood function is intractable, but where model simulation is feasible. Popular …
[ספר][B] Bayesian compendium
M Van Oijen - 2020 - Springer
The recurring issue during my four decades in science has been a struggle with
uncertainties. My primary field is agricultural, environmental and ecological science using …
uncertainties. My primary field is agricultural, environmental and ecological science using …
Bayesian modeling-based analysis on the shared habitat and species association between four Gobiidae in a marine bay ecosystem
In recent years, with the decline in marine fishery resources, ecosystem-based fisheries
management (EBFM) has emerged as an important paradigm in fisheries management …
management (EBFM) has emerged as an important paradigm in fisheries management …
Comparison of WAIC and posterior predictive approaches for N-mixture models
Hierarchical models are common for ecological analysis, but determining appropriate model
selection methods remains an ongoing challenge. To confront this challenge, a suitable …
selection methods remains an ongoing challenge. To confront this challenge, a suitable …
A Bayesian approach to correct the under-count of cancer registry statistics before population-based cancer registry program
Aim: This study aims to correct undercounts in cancer data before initiating a population-
based cancer registry program, employing an innovative Bayesian methodology …
based cancer registry program, employing an innovative Bayesian methodology …
Bayesian Design for Sampling Anomalous Spatio-Temporal Data
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 …
systems. However, the presence of anomalies can compromise the accuracy and reliability …
Adoption of Metacognitive Approach to Teaching and Learning of Programming Language Concepts to Undergraduate and Graduate University Students
Mastering programming language concepts is critical for students in computer science and
related fields. However, traditional teaching methods often fall short in fostering deep …
related fields. However, traditional teaching methods often fall short in fostering deep …
Bayesian design for sampling anomalous data on river networks
K Buchhorn - 2024 - eprints.qut.edu.au
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 …
Efficient designs for collecting high-quality, relevant data are therefore essential. This …