Results 1 to 10 of about 137,518 (176)

Approximate Bayesian Computation Via the Energy Statistic [PDF]

IEEE Access, 2020
Approximate Bayesian computation (ABC) has become an essential part of the Bayesian toolbox for addressing problems in which the likelihood is prohibitively expensive or entirely unknown, making it intractable. ABC defines a pseudo-posterior by comparing
Hien Duy Nguyen, Julyan Arbel, Hongliang Lu, Florence Forbes   +3 more
doaj   +5 more sources

Approximate Bayesian Computation for Discrete Spaces [PDF]

Entropy, 2021
Many real-life processes are black-box problems, i.e., the internal workings are inaccessible or a closed-form mathematical expression of the likelihood function cannot be defined.
Ilze A. Auzina, Jakub M. Tomczak
doaj   +2 more sources

ABCDP: Approximate Bayesian Computation with Differential Privacy [PDF]

Entropy, 2021
We developed a novel approximate Bayesian computation (ABC) framework, ABCDP, which produces differentially private (DP) and approximate posterior samples.
Mijung Park, Margarita Vinaroz, Wittawat Jitkrittum   +2 more
doaj   +2 more sources

On the asymptotic efficiency of approximate Bayesian computation estimators [PDF]

, 2017
Many statistical applications involve models for which it is difficult to evaluate the likelihood, but from which it is relatively easy to sample. Approximate Bayesian computation is a likelihood-free method for implementing Bayesian inference in such ...
Wentao Li, Paul Fearnhead
openalex   +5 more sources

Approximate Bayesian Computation by Subset Simulation [PDF]

SIAM Journal of Scientific Computing, 2014
A new Approximate Bayesian Computation (ABC) algorithm for Bayesian updating of model parameters is proposed in this paper, which combines the ABC principles with the technique of Subset Simulation for efficient rare-event simulation, first developed in ...
Manuel Chiachio, Juan Chiachio, Guillermo Rus   +2 more
exaly   +5 more sources

A wall-time minimizing parallelization strategy for approximate Bayesian computation. [PDF]

PLoS ONE
Approximate Bayesian Computation (ABC) is a widely applicable and popular approach to estimating unknown parameters of mechanistic models. As ABC analyses are computationally expensive, parallelization on high-performance infrastructure is often ...
Emad Alamoudi, Felipe Reck, Nils Bundgaard, Frederik Graw, Lutz Brusch, Jan Hasenauer, Yannik Schälte   +6 more
doaj   +4 more sources

Accounting for contact network uncertainty in epidemic inferences with Approximate Bayesian Computation [PDF]

Applied Network Science
In models of infectious disease dynamics, the incorporation of contact network information allows for the capture of the non-randomness and heterogeneity of realistic contact patterns.
Maxwell H. Wang, Jukka-Pekka Onnela
doaj   +2 more sources

Approximate Bayesian computation for inferring Waddington landscapes from single-cell data [PDF]

Royal Society Open Science
Single-cell technologies allow us to gain insights into cellular processes at unprecedented resolution. In stem cell and developmental biology snapshot data allow us to characterize how the transcriptional states of cells change between successive cell ...
Yujing Liu, Stephen Y. Zhang, Istvan T. Kleijn, Michael P. H. Stumpf   +3 more
doaj   +2 more sources

Dynamic calibration with approximate Bayesian computation for a microsimulation of disease spread [PDF]

Scientific Reports, 2023
The global COVID-19 pandemic brought considerable public and policy attention to the field of infectious disease modelling. A major hurdle that modellers must overcome, particularly when models are used to develop policy, is quantifying the uncertainty ...
Molly Asher, Nik Lomax, Karyn Morrissey, Fiona Spooner, Nick Malleson   +4 more
doaj   +2 more sources

Calibration of stochastic, agent-based neuron growth models with approximate Bayesian computation. [PDF]

J Math Biol
Duswald T, Breitwieser L, Thorne T, Wohlmuth B, Bauer R.   +4 more
europepmc   +5 more sources