Optimal Bayesian design for model discrimination via classification

Hainy, Markus; Price, David J.; Restif, Olivier; Drovandi, Christopher

doi:10.1007/s11222-022-10078-2

Cited by 3 publications

(1 citation statement)

References 46 publications

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“…If an average over nuisance variables can be used to model the intractable likelihood, the NMC method can be used in an extended form (Feng & Marzouk, 2019), but in general this will lead to high computational cost in practical problems, because in this extended form, the inner loop samples can not be reused. Alternative methods include the use of a variational approximation of the likelihood (e. g., Foster et al (2019a); Cheng et al ( 2020)), and several everal works focus exclusively on experimental design algorithms for likelihood-free experimental design (Kleinegesse & Gutmann, 2018, 2021Hainy et al, 2014Hainy et al, , 2016Hainy et al, , 2018.…”

Section: Likelihood-free Experimental Designmentioning

confidence: 99%

Variational Experimental Design Methods for Geophysical Applications

Strutz

Curtis

2023

Preprint

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<div> <p><span data-contrast="auto">The design of geophysical surveys or experiments (henceforth, the experimental design) significantly influences the uncertainty in scientific results that can be inferred from recorded data. Typical aspects of experimental designs that can be varied are locations of sensors, sensor types, and the modelling or data processing methods to be applied to recorded data. To tighten constraints on the solution to any inverse or inference problem, and thus to rule out as many false possibilities as possible, the design should be optimised such that it is practically achievable within cost and logistical constraints, and such that it maximises expected post-experimental information about the solution.</span><span data-ccp-props="{">&#160;</span></p> </div> <div> <p><span data-contrast="auto">Bayesian experimental design refers to a class of methods that use uncertainty estimation methods to quantify the expected gain in information about target parameters provided by an experiment, and to optimise the design of the experiment to maximise that gain. Information gain quantifies the decrease in uncertainty caused by observing data. Expected information gain is an estimate of the gain in information that will be offered by any particular design post-experiment. Bayesian experimental design methods vary the design so as to maximise the expected information gain, subject to practical constraints.</span><span data-ccp-props="{">&#160;</span></p> </div> <div> <p><span data-contrast="auto">We introduce variational experimental design methods that are novel to geophysics, and discuss their benefits and limitations in the context of geophysical applications. The family of variational methods relies on functional approximations of probability distributions, and in some cases, of the model-data relationships. They can be used to design experiments that best resolve either all model parameters, or the answer to a specific question about the system studied. Their potential advantage over some other design methods is that finding the functional approximations used by variational methods tends to rely more on optimisation theory than the more common stochastic uncertainty analysis used to approximate Bayesian uncertainties. This allows the wealth of understanding of optimisation methods to be applied to the full Bayesian design problem.</span><span data-ccp-props="{">&#160;</span></p> </div> <div> <p><span data-contrast="auto">Variational design methods are demonstrated by optimising the design of an experiment consisting of seismometer locations on the Earth&#8217;s surface, so as to best estimate seismic source parameters given arrival time data obtained at seismometers. By designing separate experiments to constrain the hypocentres and epicentres of events, we show that optimal designs may change substantially depending on which questions about the subsurface we wish the experiment to help us to answer.</span><span data-ccp-props="{">&#160;</span></p> </div> <div> <p><span data-contrast="auto">By accounting for differing expected uncertainties in travel time picks depending on the picking method used, we demonstrate that the data processing method can be optimised as part of the design process, provided that expected uncertainties are available from each method.</span></p> </div>

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Section: Likelihood-free Experimental Designmentioning

confidence: 99%

Variational Experimental Design Methods for Geophysical Applications

Strutz

Curtis

2023

Preprint

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A design criterion for symmetric model discrimination based on flexible nominal sets

Harman

Müller

2020

Biometrical J

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Experimental design applications for discriminating between models have been hampered by the assumption to know beforehand which model is the true one, which is counter to the very aim of the experiment. Previous approaches to alleviate this requirement were either symmetrizations of asymmetric techniques, or Bayesian, minimax and sequential approaches.Here we present a genuinely symmetric criterion based on a linearized distance between meanvalue surfaces and the newly introduced tool of flexible nominal confidence sets. We demonstrate the computational efficiency of the approach using the proposed criterion and provide a Monte-Carlo evaluation of its discrimination performance on the basis of the likelihood ratio. An application for a pair of competing models in enzyme kinetics is given.

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Identification of the relative timing of infectiousness and symptom onset for outbreak control

Cope

Ross

2020

Journal of Theoretical Biology

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a b s t r a c tIn an outbreak of an emerging disease the epidemiological characteristics of the pathogen may be largely unknown. A key determinant of ability to control the outbreak is the relative timing of infectiousness and symptom onset. We provide a method for identifying this relationship with high accuracy based on data from simulated household-stratified symptom-onset data. Further, this can be achieved with observations taken on only a few specific days, chosen optimally, within each household. The information provided by this method may inform decision making processes for outbreak response. An accurate and computationally-efficient heuristic for determining the optimal surveillance scheme is introduced. This heuristic provides a novel approach to optimal design for Bayesian model discrimination.

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Optimal Bayesian design for model discrimination via classification

Cited by 3 publications

References 46 publications

Variational Experimental Design Methods for Geophysical Applications

Variational Experimental Design Methods for Geophysical Applications

A design criterion for symmetric model discrimination based on flexible nominal sets

Identification of the relative timing of infectiousness and symptom onset for outbreak control

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