Results 1 to 10 of about 149,758 (276)
Approximate Bayesian Inference. [PDF]
Entropy (Basel), 2020 This is the Editorial article summarizing the scope of the Special Issue: Approximate Bayesian Inference.
Alquier P.
europepmc +6 more sources
Robust approximate Bayesian inference [PDF]
Journal of Statistical Planning and Inference, 2019 We discuss an approach for deriving robust posterior distributions from $M$-estimating functions using Approximate Bayesian Computation (ABC) methods.
Ruli, Erlis +2 more
core +2 more sources
Approximate Bayesian inference for complex ecosystems. [PDF]
F1000Prime Rep, 2014 Mathematical models have been central to ecology for nearly a century. Simple models of population dynamics have allowed us to understand fundamental aspects underlying the dynamics and stability of ecological systems. What has remained a challenge, however, is to meaningfully interpret experimental or observational data in light of mathematical models.
Stumpf MP.
europepmc +5 more sources
Approximate Decentralized Bayesian Inference [PDF]
, 2014 This paper presents an approximate method for performing Bayesian inference in models with conditional independence over a decentralized network of learning agents.
Campbell, Trevor, How, Jonathan P.
core +4 more sources
Robust Approximate Bayesian Inference With Synthetic Likelihood [PDF]
Journal of Computational and Graphical Statistics, 2021 Bayesian synthetic likelihood (BSL) is now an established method for conducting approximate Bayesian inference in models where, due to the intractability of the likelihood function, exact Bayesian approaches are either infeasible or computationally too demanding. Implicit in the application of BSL is the assumption that the data generating process (DGP)
David T. Frazier, Christopher Drovandi
openaire +5 more sources
Approximate Bayesian inference in semi-mechanistic models. [PDF]
Stat Comput, 2017 Inference of interaction networks represented by systems of differential equations is a challenging problem in many scientific disciplines. In the present article, we follow a semi-mechanistic modelling approach based on gradient matching. We investigate the extent to which key factors, including the kinetic model, statistical formulation and numerical
Aderhold A, Husmeier D, Grzegorczyk M.
europepmc +7 more sources
Approximate Bayesian Inference for Survival Models [PDF]
Scandinavian Journal of Statistics, 2010 Abstract. Bayesian analysis of time‐to‐event data, usually called survival analysis, has received increasing attention in the last years. In Cox‐type models it allows to use information from the full likelihood instead of from a partial likelihood, so that the baseline hazard function and the model parameters can be jointly estimated.
Martino, Sara +2 more
openaire +2 more sources
Approximated Information Analysis in Bayesian Inference [PDF]
Entropy, 2015 In models with nuisance parameters, Bayesian procedures based on Markov Chain Monte Carlo (MCMC) methods have been developed to approximate the posterior distribution of the parameter of interest. Because these procedures require burdensome computations related to the use of MCMC, approximation and convergence in these procedures are important issues ...
Seo, Jung, Kim, Yongku
openaire +2 more sources
How long, O Bayesian network, will I sample thee? A program analysis perspective on expected sampling times [PDF]
, 2018 Bayesian networks (BNs) are probabilistic graphical models for describing complex joint probability distributions. The main problem for BNs is inference: Determine the probability of an event given observed evidence.
Batz, Kevin +3 more
core +2 more sources
Approximate Bayesian Inference in Spatial Environments
Robotics: Science and Systems XV, 2019 Preprint of publication at RSS ...
Mirchev, Atanas +4 more
openaire +2 more sources