Rapid Bayesian inference for expensive stochastic models

Abstract

Almost all fields of science rely upon statistical inference to estimate unknown parameters in theoretical and computational models. While the performance of modern computer hardware continues to grow, the computational requirements for the simulation of models are growing even faster. This is largely due to the increase in model complexity, often including stochastic dynamics, that is necessary to describe and characterize phenomena observed using modern, high resolution, experimental techniques. Such models are rarely analytically tractable, meaning that extremely large numbers of stochastic simulations are required for parameter inference. In such cases, parameter inference can be practically impossible. In this work, we present new computational Bayesian techniques that accelerate inference for expensive stochastic models by using computationally inexpensive approximations to inform feasible regions in parameter space, and through learning transforms that adjust the biased approximate inferences to closer represent the correct inferences under the expensive stochastic model. Using topical examples from ecology and cell biology, we demonstrate a speed improvement of an order of magnitude without any loss in accuracy. This represents a substantial improvement over current state-of-the-art methods for Bayesian computations when appropriate model approximations are available.

References in corpus (16)

• First M87 Event Horizon Telescope Results. VI. The Shadow and Mass of the Central Black Hole
• Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems
• Adaptive approximate Bayesian computation
• A Comparative Review of Dimension Reduction Methods in Approximate Bayesian Computation
• On the utility of graphics cards to perform massively parallel simulation of advanced Monte Carlo methods
• Simulation and inference algorithms for stochastic biochemical reaction networks: from basic concepts to state-of-the-art
• Transport map accelerated Markov chain Monte Carlo
• A stochastic model for wound healing
• Delayed acceptance particle MCMC for exact inference in stochastic kinetic models
• Optimal quantification of contact inhibition in cell populations
• Multilevel rejection sampling for approximate Bayesian computation
• A practical guide to pseudo-marginal methods for computational inference in systems biology
• Multifidelity Approximate Bayesian Computation
• Surrogate Accelerated Bayesian Inversion for the Determination of the Thermal Diffusivity of a Material
• Efficient parameter sensitivity computation for spatially-extended reaction networks
• Vector operations for accelerating expensive Bayesian computations -- a tutorial guide

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• Generalised likelihood profiles for models with intractable likelihoods