scholarly journals Estimating parameters of a stochastic cell invasion model with fluorescent cell cycle labelling using Approximate Bayesian Computation

2021 ◽  
Author(s):  
Michael J Carr ◽  
Matthew J Simpson ◽  
Christopher Drovandi
Keyword(s):  
Cell Cycle ◽  
Stochastic Model ◽  
Cell Invasion ◽  
Approximate Bayesian Computation ◽  
Bayesian Computation ◽  
Trajectory Data ◽  
Fluorescent Cell ◽  
Abc Algorithm ◽  
Invasion Model ◽  
Approximate Bayesian

AbstractWe develop a parameter estimation method based on approximate Bayesian computation (ABC) for a stochastic cell invasion model using fluorescent cell cycle labeling with proliferation, migration, and crowding effects. Previously, inference has been performed on a deterministic version of the model fitted to cell density data, and not all the parameters were identifiable. Considering the stochastic model allows us to harness more features of experimental data, including cell trajectories and cell count data, which we show overcomes the parameter identifiability problem. We demonstrate that, whilst difficult to collect, cell trajectory data can provide more information about the parameters of the cell invasion model. To handle the intractability of the likelihood function of the stochastic model, we use an efficient ABC algorithm based on sequential Monte Carlo. Rcpp and MATLAB implementations of the simulation model and ABC algorithm used in this study are available athttps://github.com/michaelcarr-stats/FUCCI.

scholarly journals Estimating parameters of a stochastic cell invasion model with fluorescent cell cycle labelling using approximate Bayesian computation

2021 ◽  
Vol 18 (182) ◽  
pp. 20210362
Author(s):  
Michael J. Carr ◽  
Matthew J. Simpson ◽  
Christopher Drovandi
Keyword(s):  
Cell Cycle ◽  
Stochastic Model ◽  
Cell Invasion ◽  
Approximate Bayesian Computation ◽  
Bayesian Computation ◽  
Trajectory Data ◽  
Fluorescent Cell ◽  
Abc Algorithm ◽  
Invasion Model ◽  
Approximate Bayesian

We develop a parameter estimation method based on approximate Bayesian computation (ABC) for a stochastic cell invasion model using fluorescent cell cycle labelling with proliferation, migration and crowding effects. Previously, inference has been performed on a deterministic version of the model fitted to cell density data, and not all parameters were identifiable. Considering the stochastic model allows us to harness more features of experimental data, including cell trajectories and cell count data, which we show overcomes the parameter identifiability problem. We demonstrate that, while difficult to collect, cell trajectory data can provide more information about the parameters of the cell invasion model. To handle the intractability of the likelihood function of the stochastic model, we use an efficient ABC algorithm based on sequential Monte Carlo. Rcpp and MATLAB implementations of the simulation model and ABC algorithm used in this study are available at https://github.com/michaelcarr-stats/FUCCI .

scholarly journals ABCDP: Approximate Bayesian Computation with Differential Privacy

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 961
Author(s):  
Mijung Park ◽  
Margarita Vinaroz ◽  
Wittawat Jitkrittum
Keyword(s):  
Approximate Bayesian Computation ◽  
Differential Privacy ◽  
Simulated Data ◽  
Bayesian Computation ◽  
Abc Algorithm ◽  
Quantity Of Interest ◽  
Sparse Vector ◽  
Minimal Modification ◽  
Approximate Bayesian ◽  
Privacy Level

We developed a novel approximate Bayesian computation (ABC) framework, ABCDP, which produces differentially private (DP) and approximate posterior samples. Our framework takes advantage of the sparse vector technique (SVT), widely studied in the differential privacy literature. SVT incurs the privacy cost only when a condition (whether a quantity of interest is above/below a threshold) is met. If the condition is sparsely met during the repeated queries, SVT can drastically reduce the cumulative privacy loss, unlike the usual case where every query incurs the privacy loss. In ABC, the quantity of interest is the distance between observed and simulated data, and only when the distance is below a threshold can we take the corresponding prior sample as a posterior sample. Hence, applying SVT to ABC is an organic way to transform an ABC algorithm to a privacy-preserving variant with minimal modification, but yields the posterior samples with a high privacy level. We theoretically analyzed the interplay between the noise added for privacy and the accuracy of the posterior samples. We apply ABCDP to several data simulators and show the efficacy of the proposed framework.

Modelling coronal mass ejection flux ropes signatures using Approximate Bayesian Computation: applications to Parker Solar Probe

2020 ◽  
Author(s):  
Andreas Weiss ◽  
Christian Möstl ◽  
Teresa Nieves-Chinchilla ◽  
Tanja Amerstorfer ◽  
Erika Palmerio ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Coronal Mass Ejection ◽  
Approximate Bayesian Computation ◽  
Flux Rope ◽  
Bayesian Computation ◽  
Abc Algorithm ◽  
Flux Ropes ◽  
Approximate Bayesian ◽  
Mass Ejection

<p>We present an updated three-dimensional coronal rope ejection (3DCORE) model and an associated pipeline that is capable of producing extremely large ensembles of synthetic in-situ magnetic field measurements from simulated coronal mass ejection flux ropes. The model assumes an empirically motivated torus-like flux rope structure that expands self-similarly and contains an embedded analytical magnetic field. Using an Approximate Bayesian computation (ABC) algorithm we validate the model by showing that it is capable of qualitatively reproducing measured flux rope signatures. The ABC algorithm also gives us uncertainty estimates in the form of probability distributions for all model parameters. We show the first results for applying our model and algorithms to coronal mass ejections observed in situ by Parker Solar Probe, specifically the event on 2018 November 12 at 0.26AU, where we attempt to reproduce the measured magnetic field signatures and furthermore reconstruct the global flux rope geometry.</p>

scholarly journals Weighted approximate Bayesian computation via Sanov’s theorem

2021 ◽  
Author(s):  
Cecilia Viscardi ◽  
Michele Boreale ◽  
Fabio Corradi
Keyword(s):  
Large Deviations ◽  
Posterior Distribution ◽  
Approximate Bayesian Computation ◽  
Bayesian Computation ◽  
Information Theoretic ◽  
Discrete Random Variables ◽  
Positive Weights ◽  
Approximate Bayesian ◽  
Information Theoretic Method ◽  
Computational Resources

AbstractWe consider the problem of sample degeneracy in Approximate Bayesian Computation. It arises when proposed values of the parameters, once given as input to the generative model, rarely lead to simulations resembling the observed data and are hence discarded. Such “poor” parameter proposals do not contribute at all to the representation of the parameter’s posterior distribution. This leads to a very large number of required simulations and/or a waste of computational resources, as well as to distortions in the computed posterior distribution. To mitigate this problem, we propose an algorithm, referred to as the Large Deviations Weighted Approximate Bayesian Computation algorithm, where, via Sanov’s Theorem, strictly positive weights are computed for all proposed parameters, thus avoiding the rejection step altogether. In order to derive a computable asymptotic approximation from Sanov’s result, we adopt the information theoretic “method of types” formulation of the method of Large Deviations, thus restricting our attention to models for i.i.d. discrete random variables. Finally, we experimentally evaluate our method through a proof-of-concept implementation.

Flow parameter estimation using laser absorption spectroscopy and approximate Bayesian computation

2021 ◽  
Vol 62 (2) ◽  
Author(s):  
Jason D. Christopher ◽  
Olga A. Doronina ◽  
Dan Petrykowski ◽  
Torrey R. S. Hayden ◽  
Caelan Lapointe ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Absorption Spectroscopy ◽  
Approximate Bayesian Computation ◽  
Flow Parameter ◽  
Bayesian Computation ◽  
Laser Absorption Spectroscopy ◽  
Laser Absorption ◽  
Approximate Bayesian

scholarly journals Approximate Bayesian Computation for Discrete Spaces

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 312
Author(s):  
Ilze A. Auzina ◽  
Jakub M. Tomczak
Keyword(s):  
Approximate Bayesian Computation ◽  
Likelihood Function ◽  
Real Life ◽  
Random Variables ◽  
Bayesian Computation ◽  
Inference Problem ◽  
Markov Kernel ◽  
Inference Problems ◽  
Binary Neural Network ◽  
Approximate Bayesian

Many real-life processes are black-box problems, i.e., the internal workings are inaccessible or a closed-form mathematical expression of the likelihood function cannot be defined. For continuous random variables, likelihood-free inference problems can be solved via Approximate Bayesian Computation (ABC). However, an optimal alternative for discrete random variables is yet to be formulated. Here, we aim to fill this research gap. We propose an adjusted population-based MCMC ABC method by re-defining the standard ABC parameters to discrete ones and by introducing a novel Markov kernel that is inspired by differential evolution. We first assess the proposed Markov kernel on a likelihood-based inference problem, namely discovering the underlying diseases based on a QMR-DTnetwork and, subsequently, the entire method on three likelihood-free inference problems: (i) the QMR-DT network with the unknown likelihood function, (ii) the learning binary neural network, and (iii) neural architecture search. The obtained results indicate the high potential of the proposed framework and the superiority of the new Markov kernel.

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