Interpreting scratch assays using pair density dynamics and approximate Bayesian computation [PDF]
Open Biology, 2014 Quantifying the impact of biochemical compounds on collective cell spreading is an essential element of drug design, with various applications including developing treatments for chronic wounds and cancer.
Stuart T. Johnston +4 more
doaj +1 more source
Modeling of Flowering Time in Vigna radiata with Approximate Bayesian Computation
Agronomy, 2021 Flowering time is an important target for breeders in developing new varieties adapted to changing conditions. A new approach is proposed that uses Approximate Bayesian Computation with Differential Evolution to construct a pool of models for flowering ...
Andrey Ageev +7 more
doaj +1 more source
Approximate Bayesian Computation for a Class of Time Series Models [PDF]
, 2014 In the following article we consider approximate Bayesian computation (ABC) for certain classes of time series models. In particular, we focus upon scenarios where the likelihoods of the observations and parameter are intractable, by which we mean that ...
Jasra, Ajay
core +1 more source
Cellular connectomes as arbiters of local circuit models in the cerebral cortex
Nature Communications, 2021 Large-scale connectomes from the mammalian brain are becoming available, but it remains unclear how informative these are for the distinction of circuit models.
Emmanuel Klinger +4 more
doaj +1 more source
The rate of convergence for approximate Bayesian computation [PDF]
Electronic Journal of Statistics, 2015 25 pages, 3 figures; address the distinction between fixed number of proposals and fixed number of accepted samples more ...
Barber, Stuart +2 more
openaire +5 more sources
Approximate Bayesian computing for spatial extremes
Computational Statistics & Data Analysis, 2012 Statistical analysis of max-stable processes used to model spatial extremes has been limited by the difficulty in calculating the joint likelihood function. This precludes all standard likelihood-based approaches, including Bayesian approaches. In this paper we present a Bayesian approach through the use of approximate Bayesian computing.
Robert J. Erhardt, Richard L. Smith
openaire +3 more sources
Approximate Bayesian Computation with the Sliced-Wasserstein Distance [PDF]
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020 Approximate Bayesian Computation (ABC) is a popular method for approximate inference in generative models with intractable but easy-to-sample likelihood. It constructs an approximate posterior distribution by finding parameters for which the simulated data are close to the observations in terms of summary statistics.
Nadjahi, Kimia +4 more
openaire +3 more sources
A Comparative Review of Dimension Reduction Methods in Approximate Bayesian Computation [PDF]
, 2013 Approximate Bayesian computation (ABC) methods make use of comparisons between simulated and observed summary statistics to overcome the problem of computationally intractable likelihood functions.
Blum, M. G. B. +3 more
core +4 more sources
Regression approaches for Approximate Bayesian Computation [PDF]
, 2017 This book chapter introduces regression approaches and regression adjustment for Approximate Bayesian Computation (ABC). Regression adjustment adjusts parameter values after rejection sampling in order to account for the imperfect match between ...
Blum, Michael GB
core +1 more source
Quick and energy-efficient Bayesian computing of binocular disparity using stochastic digital signals [PDF]
, 2016 Reconstruction of the tridimensional geometry of a visual scene using the binocular disparity information is an important issue in computer vision and mobile robotics, which can be formulated as a Bayesian inference problem.
Bessière, Pierre +2 more
core +5 more sources