Results 11 to 20 of about 135,519 (174)
Approximate Bayesian computation for inferring Waddington landscapes from single-cell data [PDF]
Royal Society Open ScienceSingle-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 +3 more
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Informative and adaptive distances and summary statistics in sequential approximate Bayesian computation. [PDF]
PLoS ONE, 2023 Calibrating model parameters on heterogeneous data can be challenging and inefficient. This holds especially for likelihood-free methods such as approximate Bayesian computation (ABC), which rely on the comparison of relevant features in simulated and ...
Yannik Schälte, Jan Hasenauer
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An automatic adaptive method to combine summary statistics in approximate Bayesian computation. [PDF]
PLoS ONE, 2020 To infer the parameters of mechanistic models with intractable likelihoods, techniques such as approximate Bayesian computation (ABC) are increasingly being adopted. One of the main disadvantages of ABC in practical situations, however, is that parameter
Jonathan U Harrison, Ruth E Baker
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Multifidelity Approximate Bayesian Computation [PDF]
SIAM/ASA Journal on Uncertainty Quantification, 2020 25 pages plus Supplementary Material (as appendices)
Prescott, T, Baker, R
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Approximate Bayesian Computation [PDF]
Annual Review of Statistics and Its Application, 2019 Many of the statistical models that could provide an accurate, interesting, and testable explanation for the structure of a data set turn out to have intractable likelihood functions. The method of approximate Bayesian computation (ABC) has become a popular approach for tackling such models.
Osvaldo A. Martin +2 more
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Hierarchical Approximate Bayesian Computation [PDF]
Psychometrika, 2014 Approximate Bayesian computation (ABC) is a powerful technique for estimating the posterior distribution of a model’s parameters. It is especially important when the model to be fit has no explicit likelihood function, which happens for computational (or simulation-based) models such as those that are popular in cognitive neuroscience and other areas ...
Turner, Brandon M., Van Zandt, Trisha
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Approximate Bayesian computational methods [PDF]
Statistics and Computing, 2011 7 ...
Marin, Jean-Michel +3 more
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Bayesian parameter inference and model selection by population annealing in systems biology. [PDF]
PLoS ONE, 2014 Parameter inference and model selection are very important for mathematical modeling in systems biology. Bayesian statistics can be used to conduct both parameter inference and model selection.
Yohei Murakami
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Software for Bayesian Statistics
Journal of Statistical Software, 2021 In this summary we introduce the papers published in the special issue on Bayesian statistics. This special issue comprises 20 papers on Bayesian statistics and Bayesian inference on different topics such as general packages for hierarchical linear model
Michela Cameletti, Virgilio Gómez-Rubio
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Adaptive Approximate Bayesian Computation Tolerance Selection [PDF]
Bayesian Analysis, 2021 Approximate Bayesian Computation (ABC) methods are increasingly used for inference in situations in which the likelihood function is either computationally costly or intractable to evaluate. Extensions of the basic ABC rejection algorithm have improved the computational efficiency of the procedure and broadened its applicability.
Simola, Umberto +3 more
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