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Approximate bayesian computation
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 …
Fundamentals and recent developments in approximate Bayesian computation
Bayesian inference plays an important role in phylogenetics, evolutionary biology, and in
many other branches of science. It provides a principled framework for dealing with …
many other branches of science. It provides a principled framework for dealing with …
SBI--A toolkit for simulation-based inference
Scientists and engineers employ stochastic numerical simulators to model empirically
observed phenomena. In contrast to purely statistical models, simulators express scientific …
observed phenomena. In contrast to purely statistical models, simulators express scientific …
[ספר][B] Handbook of approximate Bayesian computation
SA Sisson, Y Fan, M Beaumont - 2018 - books.google.com
As the world becomes increasingly complex, so do the statistical models required to analyse
the challenging problems ahead. For the very first time in a single volume, the Handbook of …
the challenging problems ahead. For the very first time in a single volume, the Handbook of …
Convolutional neural network for seismic impedance inversion
We have addressed the geophysical problem of obtaining an elastic model of the
subsurface from recorded normal-incidence seismic data using convolutional neural …
subsurface from recorded normal-incidence seismic data using convolutional neural …
Automatic posterior transformation for likelihood-free inference
How can one perform Bayesian inference on stochastic simulators with intractable
likelihoods? A recent approach is to learn the posterior from adaptively proposed …
likelihoods? A recent approach is to learn the posterior from adaptively proposed …
Benchmarking simulation-based inference
Recent advances in probabilistic modelling have led to a large number of simulation-based
inference algorithms which do not require numerical evaluation of likelihoods. However, a …
inference algorithms which do not require numerical evaluation of likelihoods. However, a …
Sequential neural likelihood: Fast likelihood-free inference with autoregressive flows
Abstract We present Sequential Neural Likelihood (SNL), a new method for Bayesian
inference in simulator models, where the likelihood is intractable but simulating data from …
inference in simulator models, where the likelihood is intractable but simulating data from …
Training deep neural density estimators to identify mechanistic models of neural dynamics
Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of
underlying causes. However, determining which model parameters agree with complex and …
underlying causes. However, determining which model parameters agree with complex and …
Field-level simulation-based inference of galaxy clustering with convolutional neural networks
We present the first simulation-based inference (SBI) of cosmological parameters from field-
level analysis of galaxy clustering. Standard galaxy clustering analyses rely on analyzing …
level analysis of galaxy clustering. Standard galaxy clustering analyses rely on analyzing …