Normalizing flows for probabilistic modeling and inference
Normalizing flows provide a general mechanism for defining expressive probability
distributions, only requiring the specification of a (usually simple) base distribution and a …
distributions, only requiring the specification of a (usually simple) base distribution and a …
Robot learning from randomized simulations: A review
The rise of deep learning has caused a paradigm shift in robotics research, favoring
methods that require large amounts of data. Unfortunately, it is prohibitively expensive to …
methods that require large amounts of data. Unfortunately, it is prohibitively expensive to …
Real-time gravitational wave science with neural posterior estimation
We demonstrate unprecedented accuracy for rapid gravitational wave parameter estimation
with deep learning. Using neural networks as surrogates for Bayesian posterior distributions …
with deep learning. Using neural networks as surrogates for Bayesian posterior distributions …
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 …
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 …
Simulation intelligence: Towards a new generation of scientific methods
The original" Seven Motifs" set forth a roadmap of essential methods for the field of scientific
computing, where a motif is an algorithmic method that captures a pattern of computation …
computing, where a motif is an algorithmic method that captures a pattern of computation …
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 …
BayesFlow: Learning complex stochastic models with invertible neural networks
Estimating the parameters of mathematical models is a common problem in almost all
branches of science. However, this problem can prove notably difficult when processes and …
branches of science. However, this problem can prove notably difficult when processes and …
Truncated proposals for scalable and hassle-free simulation-based inference
M Deistler, PJ Goncalves… - Advances in Neural …, 2022 - proceedings.neurips.cc
Simulation-based inference (SBI) solves statistical inverse problems by repeatedly running a
stochastic simulator and inferring posterior distributions from model-simulations. To improve …
stochastic simulator and inferring posterior distributions from model-simulations. To improve …
Likelihood-free mcmc with amortized approximate ratio estimators
Posterior inference with an intractable likelihood is becoming an increasingly common task
in scientific domains which rely on sophisticated computer simulations. Typically, these …
in scientific domains which rely on sophisticated computer simulations. Typically, these …