Accelerated regression-based summary statistics for discrete stochastic systems via approximate simulators

Accelerated regression-based summary statistics for discrete stochastic systems via approximate simulators

Background: Approximate Bayesian Computation (ABC) has become a key tool for calibrating the parameters of discrete stochastic biochemical models. For higher dimensional models and data, its performance is strongly dependent on having a representative set of summary statistics. While regression-base...

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Bibliographic Details
Main Authors: Hellander, A. (Author), Jiang, R.M (Author), Petzold, L.R (Author), Singh, P. (Author), Wrede, F. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2021
Subjects:
algorithm
Algorithms
Approximate algorithms
Approximate Bayesian Computation
Automatic construction
Bayes theorem
Bayes Theorem
Bayesian networks
Biochemical reaction systems
Biochemistry
biological model
computer simulation
Computer Simulation
Discrete stochastic models
Discrete stochastic reaction systems
Discrete stochastic systems
Estimating parameters
Gillespie algorithm
Machine learning
Markov chain
Models, Biological
Ordinary differential equations
Parameter estimation
regression analysis
Regression Analysis
Simulators
State-of-the-art methods
Statistics
Stochastic biochemical systems
Stochastic models
Stochastic Processes
Stochastic simulators
Stochastic systems
Summary statistics
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