Results 281 to 290 of about 137,667 (314)

Stochastic Compartmental Modelling of SARS-CoV-2 with Approximate Bayesian Computation

, 2020
Chandra V.
europepmc   +1 more source

Time-Variant Parameter Identification of Concrete Structures via Physics-Guided Temporal Decomposition and Adaptive Approximate Bayesian Computation with Sequential Monte Carlo

Meng Zhang, Jian Wang, Peng Du, Yanming Zhang, Yang Lu   +4 more
openalex   +1 more source

Not in Rivers But in Drops - Approximate Bayesian Computation with Simultaneous Selection of Sufficient Statistics

, 2011
I. N. Atanasov
openalex   +1 more source

Approximate Bayesian Computation with the Wasserstein Distance [PDF]

Journal of the Royal Statistical Society Series B: Statistical Methodology, 2019
SummaryA growing number of generative statistical models do not permit the numerical evaluation of their likelihood functions. Approximate Bayesian computation has become a popular approach to overcome this issue, in which one simulates synthetic data sets given parameters and compares summaries of these data sets with the corresponding observed values.
Espen Bernton, Pierre E Jacob, Mathieu Gerber   +2 more
exaly   +6 more sources

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A tutorial on approximate Bayesian computation

Journal of Mathematical Psychology, 2012
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Brandon M Turner, Trisha Van Zandt
exaly   +2 more sources

Filtering via approximate Bayesian computation

Statistics and Computing, 2010
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ajay Jasra, Sumeetpal S. Singh, James S. Martin, Emma J. McCoy   +3 more
openaire   +2 more sources

Approximately Sufficient Statistics and Bayesian Computation

Statistical Applications in Genetics and Molecular Biology, 2008
The analysis of high-dimensional data sets is often forced to rely upon well-chosen summary statistics. A systematic approach to choosing such statistics, which is based upon a sound theoretical framework, is currently lacking. In this paper we develop a sequential scheme for scoring statistics according to whether their inclusion in the analysis will ...
Paul, Joyce, Paul, Marjoram
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Diffusion filtration with approximate Bayesian computation

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015
Distributed filtration of state-space models with sensor networks assumes knowledge of a model of the data-generating process. However, this assumption is often violated in practice, as the conditions vary from node to node and are usually only partially known.
Kamil Dedecius, Petar M. Djuric
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An Introduction to Approximate Bayesian Computation

2019
Many modern statistical settings feature the analysis of data that may arise from unknown generating processes, or processes for which the generative models are computationally infeasible to interact with. Conventional estimation and inference solution methods in such settings may be unwieldy or impossible to implement.
openaire   +2 more sources