scholarly journals Parameter Inference for Computational Cognitive Models with Approximate Bayesian Computation

2019 ◽  
Vol 43 (6) ◽  
Author(s):  
Antti Kangasrääsiö ◽  
Jussi P. P. Jokinen ◽  
Antti Oulasvirta ◽  
Andrew Howes ◽  
Samuel Kaski
Keyword(s):  
Approximate Bayesian Computation ◽  
Cognitive Models ◽  
Bayesian Computation ◽  
Parameter Inference ◽  
Approximate Bayesian

scholarly journals GpABC: a Julia package for approximate Bayesian computation with Gaussian process emulation

2019 ◽  
Author(s):  
Evgeny Tankhilevich ◽  
Jonathan Ish-Horowicz ◽  
Tara Hameed ◽  
Elisabeth Roesch ◽  
Istvan Kleijn ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Stochastic Models ◽  
Approximate Bayesian Computation ◽  
Computational Cost ◽  
Bayesian Computation ◽  
Parameter Inference ◽  
Likelihood Functions ◽  
Gaussian Process Emulation ◽  
Selection For ◽  
Approximate Bayesian

ABSTRACTApproximate Bayesian computation (ABC) is an important framework within which to infer the structure and parameters of a systems biology model. It is especially suitable for biological systems with stochastic and nonlinear dynamics, for which the likelihood functions are intractable. However, the associated computational cost often limits ABC to models that are relatively quick to simulate in practice. We here present a Julia package, GpABC, that implements parameter inference and model selection for deterministic or stochastic models using i) standard rejection ABC or ABC-SMC, or ii) ABC with Gaussian process emulation. The latter significantly reduces the computational cost.URL: https://github.com/tanhevg/GpABC.jl

scholarly journals GpABC: a Julia package for approximate Bayesian computation with Gaussian process emulation

2020 ◽  
Vol 36 (10) ◽  
pp. 3286-3287 ◽  
Author(s):  
Evgeny Tankhilevich ◽  
Jonathan Ish-Horowicz ◽  
Tara Hameed ◽  
Elisabeth Roesch ◽  
Istvan Kleijn ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Approximate Bayesian Computation ◽  
Sequential Monte Carlo ◽  
Computational Cost ◽  
Bayesian Computation ◽  
Parameter Inference ◽  
Likelihood Functions ◽  
Gaussian Process Emulation ◽  
Selection For ◽  
Approximate Bayesian

Abstract Motivation Approximate Bayesian computation (ABC) is an important framework within which to infer the structure and parameters of a systems biology model. It is especially suitable for biological systems with stochastic and nonlinear dynamics, for which the likelihood functions are intractable. However, the associated computational cost often limits ABC to models that are relatively quick to simulate in practice. Results We here present a Julia package, GpABC, that implements parameter inference and model selection for deterministic or stochastic models using (i) standard rejection ABC or sequential Monte Carlo ABC or (ii) ABC with Gaussian process emulation. The latter significantly reduces the computational cost. Availability and implementation https://github.com/tanhevg/GpABC.jl.

scholarly journals Curve Registration of Functional Data for Approximate Bayesian Computation

Stats ◽  
2021 ◽  
Vol 4 (3) ◽  
pp. 762-775
Author(s):  
Anthony Ebert ◽  
Kerrie Mengersen ◽  
Fabrizio Ruggeri ◽  
Paul Wu
Keyword(s):  
Objective Function ◽  
Functional Data ◽  
Approximate Bayesian Computation ◽  
Probability Distributions ◽  
Distance Measures ◽  
Bayesian Computation ◽  
Time Axis ◽  
Parameter Inference ◽  
Curve Registration ◽  
Approximate Bayesian

Approximate Bayesian computation is a likelihood-free inference method which relies on comparing model realisations to observed data with informative distance measures. We obtain functional data that are not only subject to noise along their y axis but also to a random warping along their x axis, which we refer to as the time axis. Conventional distances on functions, such as the L2 distance, are not informative under these conditions. The Fisher–Rao metric, previously generalised from the space of probability distributions to the space of functions, is an ideal objective function for aligning one function to another by warping the time axis. We assess the usefulness of alignment with the Fisher–Rao metric for approximate Bayesian computation with four examples: two simulation examples, an example about passenger flow at an international airport, and an example of hydrological flow modelling. We find that the Fisher–Rao metric works well as the objective function to minimise for alignment; however, once the functions are aligned, it is not necessarily the most informative distance for inference. This means that likelihood-free inference may require two distances: one for alignment and one for parameter inference.

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