
Correlated pseudomarginal schemes for timediscretised stochastic kinetic models
Performing fully Bayesian inference for the reaction rate constants gove...
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Particle Methods for Stochastic Differential Equation Mixed Effects Models
Parameter inference for stochastic differential equation mixed effects m...
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Marginal PseudoLikelihood Learning of Markov Network structures
Undirected graphical models known as Markov networks are popular for a w...
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Bayesian Computation with Intractable Likelihoods
This article surveys computational methods for posterior inference with ...
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Learning quantitative sequencefunction relationships from massively parallel experiments
A fundamental aspect of biological information processing is the ubiquit...
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Rapid Bayesian inference for expensive stochastic models
Almost all fields of science rely upon statistical inference to estimate...
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Optimal statistical inference in the presence of systematic uncertainties using neural network optimization based on binned Poisson likelihoods with nuisance parameters
Data analysis in science, e.g., highenergy particle physics, is often s...
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A practical guide to pseudomarginal methods for computational inference in systems biology
For many stochastic models of interest in systems biology, such as those describing biochemical reaction networks, exact quantification of parameter uncertainty through statistical inference is intractable. Likelihoodfree computational inference techniques enable parameter inference when the likelihood function for the model is intractable but the generation of many sample paths is feasible through stochastic simulation of the forward problem. The most common likelihoodfree method in systems biology is approximate Bayesian computation that accepts parameters that result in low discrepancy between stochastic simulations and measured data. However, it can be difficult to assess how the accuracy of the resulting inferences are affected by the choice of acceptance threshold and discrepancy function. The pseudomarginal approach is an alternative likelihoodfree inference method that utilises a Monte Carlo estimate of the likelihood function. This approach has several advantages, particularly in the context of noisy, partially observed, timecourse data typical in biochemical reaction network studies. Specifically, the pseudomarginal approach facilitates exact inference and uncertainty quantification, and may be efficiently combined with particle filters for low variance, highaccuracy likelihood estimation. In this review, we provide a practical introduction to the pseudomarginal approach using inference for biochemical reaction networks as a series of case studies. Implementations of key algorithms and examples are provided using the Julia programming language; a high performance, open source programming language for scientific computing.
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