Multi-level methods and approximating distribution functions

• Published in: AIP advances Vol. 6; no. 7
• Main Authors: Wilson, D., Baker, R. E.
• Format: Journal Article
• Language: English
• Published: Melville American Institute of Physics 01.07.2016
• Subjects: ACCURACY; Algorithms; Approximation; APPROXIMATIONS; Bias; COMPARATIVE EVALUATIONS; Computation; Computer simulation; CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND SUPERFLUIDITY; Continuous time systems; CORRECTIONS; Costs; DISTRIBUTION; DISTRIBUTION FUNCTIONS; Estimates; Markov chains; MARKOV PROCESS; Methods; MONTE CARLO METHOD; Monte Carlo simulation; Multiscale analysis; PROBABILITY; REDUCTION; SIMULATION; Statistical analysis; Statistics
• ISSN: 2158-3226; 2158-3226
• DOI: 10.1063/1.4960118

Biochemical reaction networks are often modelled using discrete-state, continuous-time Markov chains. System statistics of these Markov chains usually cannot be calculated analytically and therefore estimates must be generated via simulation techniques. There is a well documented class of simulation techniques known as exact stochastic simulation algorithms, an example of which is Gillespie’s direct method. These algorithms often come with high computational costs, therefore approximate stochastic simulation algorithms such as the tau-leap method are used. However, in order to minimise the bias in the estimates generated using them, a relatively small value of tau is needed, rendering the computational costs comparable to Gillespie’s direct method. The multi-level Monte Carlo method (Anderson and Higham, Multiscale Model. Simul. 10:146–179, 2012) provides a reduction in computational costs whilst minimising or even eliminating the bias in the estimates of system statistics. This is achieved by first crudely approximating required statistics with many sample paths of low accuracy. Then correction terms are added until a required level of accuracy is reached. Recent literature has primarily focussed on implementing the multi-level method efficiently to estimate a single system statistic. However, it is clearly also of interest to be able to approximate entire probability distributions of species counts. We present two novel methods that combine known techniques for distribution reconstruction with the multi-level method. We demonstrate the potential of our methods using a number of examples.