Corpus ID: 231717221

Simulation-Based Inference with Approximately Correct Parameters via Maximum Entropy

  title={Simulation-Based Inference with Approximately Correct Parameters via Maximum Entropy},
  author={Rainier Barrett and Mehrad Gholizadeh Ansari and Gourab Ghoshal and Andrew D White},
Inferring the input parameters of simulators from observations is a crucial challenge with applications from epidemiology to molecular dynamics. Here we show a simple approach in the regime of sparse data and approximately correct models, which is common when trying to use an existing model to infer latent variables with observed data. This approach is based on the principle of maximum entropy and provably makes the smallest change in the latent joint distribution to accommodate new data. This… Expand

Figures from this paper


SBI - A toolkit for simulation-based inference
A PyTorch-based package that implements SBI algorithms based on neural networks facilitates inference on black-box simulators for practising scientists and engineers by providing a unified interface to state-of-the-art algorithms together with documentation and tutorials. Expand
Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems
This paper discusses and applies an ABC method based on sequential Monte Carlo (SMC) to estimate parameters of dynamical models and develops ABC SMC as a tool for model selection; given a range of different mathematical descriptions, it is able to choose the best model using the standard Bayesian model selection apparatus. Expand
Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows
It is shown that SNL is more robust, more accurate and requires less tuning than related neural-based methods, and diagnostics for assessing calibration, convergence and goodness-of-fit are discussed. Expand
On the statistical equivalence of restrained-ensemble simulations with the maximum entropy method.
It is demonstrated that the statistical distribution produced by restrained-ensemble simulations is formally consistent with the maximum entropy method of Jaynes, which clarifies the underlying conditions under which restrained-ensingmble simulations will yield results that are consistentwith themaximum entropy method. Expand
A tutorial introduction to Bayesian inference for stochastic epidemic models using Approximate Bayesian Computation.
This paper reviews recent Approximate Bayesian Computation methods for the analysis of disease outbreak data by fitting to them stochastic epidemic models without having to calculate the likelihood of the observed data. Expand
Integrating Molecular Simulation and Experimental Data: A Bayesian/Maximum Entropy reweighting approach
A practical guide on how to obtain and use optimized weights that can be used to calculate arbitrary properties and distributions of a conformational ensemble of a biomolecular system is provided. Expand
Approximate Bayesian computation in population genetics.
A key advantage of the method is that the nuisance parameters are automatically integrated out in the simulation step, so that the large numbers of nuisance parameters that arise in population genetics problems can be handled without difficulty. Expand
Bayesian Deep Learning and a Probabilistic Perspective of Generalization
It is shown that deep ensembles provide an effective mechanism for approximate Bayesian marginalization, and a related approach is proposed that further improves the predictive distribution by marginalizing within basins of attraction, without significant overhead. Expand
Bayesian ensemble refinement by replica simulations and reweighting.
It is shown that the strength of the restraints should scale linearly with the number of replicas to ensure convergence to the optimal Bayesian result in the limit of infinitely many replicas. Expand
Monte Carlo Methods of Inference for Implicit Statistical Models
A prescribed statistical model is a parametric specification of the distribution of a random vector, whilst an implicit statistical model is one defined at a more fundamental level in terms of aExpand