Approximate bayesian computation
MA Beaumont - Annual review of statistics and its application, 2019 - annualreviews.org
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 …
explanation for the structure of a data set turn out to have intractable likelihood functions …
Considering discrepancy when calibrating a mechanistic electrophysiology model
Uncertainty quantification (UQ) is a vital step in using mathematical models and simulations
to take decisions. The field of cardiac simulation has begun to explore and adopt UQ …
to take decisions. The field of cardiac simulation has begun to explore and adopt UQ …
Overview of ABC
This chapter explains an intuitive exploration of the basics of approximate Bayesian
computation (ABC) methods, and illustrates wherever possible by simple examples. It …
computation (ABC) methods, and illustrates wherever possible by simple examples. It …
Approximate Bayesian computation and simulation-based inference for complex stochastic epidemic models
Approximate Bayesian Computation and Simulation-Based Inference for Complex Stochastic
Epidemic Models Page 1 Statistical Science 2018, Vol. 33, No. 1, 4–18 https://doi.org/10.1214/17-STS618 …
Epidemic Models Page 1 Statistical Science 2018, Vol. 33, No. 1, 4–18 https://doi.org/10.1214/17-STS618 …
Sequential neural score estimation: Likelihood-free inference with conditional score based diffusion models
We introduce Sequential Neural Posterior Score Estimation (SNPSE) and Sequential Neural
Likelihood Score Estimation (SNLSE), two new score-based methods for Bayesian inference …
Likelihood Score Estimation (SNLSE), two new score-based methods for Bayesian inference …
[HTML][HTML] Filter inference: A scalable nonlinear mixed effects inference approach for snapshot time series data
Variability is an intrinsic property of biological systems and is often at the heart of their
complex behaviour. Examples range from cell-to-cell variability in cell signalling pathways to …
complex behaviour. Examples range from cell-to-cell variability in cell signalling pathways to …
An approximate likelihood perspective on ABC methods
G Karabatsos, F Leisen - 2018 - projecteuclid.org
We are living in the big data era, as current technologies and networks allow for the easy
and routine collection of data sets in different disciplines. Bayesian Statistics offers a flexible …
and routine collection of data sets in different disciplines. Bayesian Statistics offers a flexible …
PLASIM–GENIE v1. 0: a new intermediate complexity AOGCM
PB Holden, NR Edwards, K Fraedrich… - Geoscientific Model …, 2016 - gmd.copernicus.org
We describe the development, tuning and climate of Planet Simulator (PLASIM)–Grid-
ENabled Integrated Earth system model (GENIE), a new intermediate complexity …
ENabled Integrated Earth system model (GENIE), a new intermediate complexity …
Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation
M Jarvenpaa, A Vehtari… - … on Uncertainty in …, 2020 - proceedings.mlr.press
The computational efficiency of approximate Bayesian computation (ABC) has been
improved by using surrogate models such as Gaussian processes (GP). In one such …
improved by using surrogate models such as Gaussian processes (GP). In one such …
Known boundary emulation of complex computer models
I Vernon, SE Jackson, JA Cumming - SIAM/ASA Journal on Uncertainty …, 2019 - SIAM
Computer models are now widely used across a range of scientific disciplines to describe
various complex physical systems; however, to perform full uncertainty quantification we …
various complex physical systems; however, to perform full uncertainty quantification we …