
Probabilistic Inference on Noisy Time Series (PINTS)
References
- Adam, B, Bauman, L, Bohnhoff, W, Dalbey, K, Ebeida, M, et al. 2015
‘Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis: Version 6.0 user manual’ , Technical report, Tech. rep., Sandia NationalLaboratories. DOI: 10.2172/1177048 - Carpenter, B, Gelman, A, Hoffman, M D, Lee, D, Goodrich, B, Betancourt, M, Brubaker, M, Guo, J, Li, P and Riddell, A 2017 ‘Stan: A probabilistic programming language’. Journal of Statistical Software, 76(1). DOI: 10.18637/jss.v076.i01
- Foreman-Mackey, D, Hogg, D W, Lang, D and Goodman, J 2013 ‘emcee: the MCMC hammer’. Publications of the Astronomical Society of the Pacific, 125(925): 306. DOI: 10.1086/670067
- Girolami, M and Calderhead, B 2011 ‘Riemann manifold Langevin and Hamiltonian Monte Carlo methods’. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 73(2): 123–214. DOI: 10.1111/j.1467-9868.2010.00765.x
- Glasmachers, T, Schaul, T, Yi, S, Wierstra, D and Schmidhuber, J 2010 ‘Exponential natural evolution strategies’. In: ‘Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation’, ACM, 393–400. DOI: 10.1145/1830483.1830557
- Hansen, N, Müller, S D and Koumoutsakos, P 2003 ‘Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES)’. Evolutionary Computation, 11(1): 1–18. DOI: 10.1162/106365603321828970
- Hunter, J D 2007 ‘Matplotlib: A 2D graphics environment’. Computing in Science & Engineering, 9(3): 90–95. DOI: 10.1109/MCSE.2007.55
- Jasra, A, Stephens, D A and Holmes, C C 2007 ‘On population-based simulation for static inference’. Statistics and Computing, 17(3): 263–279. DOI: 10.1007/s11222-007-9028-9
- Johnson, R, Kirk, P and Stumpf, M P 2014 ‘SYSBIONS: nested sampling for systems biology’. Bioinformatics, 31(4): 604–605. DOI: 10.1093/bioinformatics/btu675
- Johnstone, R H, Chang, E T, Bardenet, R, De Boer, T P, Gavaghan, D J, Pathmanathan, P, Clayton, R H and Mirams, G R 2016. ‘Uncertainty and variability in models of the cardiac action potential: Can we build trustworthy models?’ Journal of Molecular and Cellular Cardiology, 96: 49–62. DOI: 10.1113/JP271671
- Jones, E, Oliphant, T and Peterson, P 2014 ‘Scipy: open source scientific tools for Python’.
http://www.scipy.org . [Online; accessed 13-Aug-2018]. - Kennedy, J 2011
Particle swarm optimization . In: ‘Encyclopedia of Machine Learning’, Springer, 760–766. DOI: 10.1007/978-0-387-30164-8_630 - Lambert, B 2018 ‘A Student’s Guide to Bayesian Statistics’. Sage Publications Ltd.
- Liepe, J, Barnes, C, Cule, E, Erguler, K, Kirk, P, Toni, T and Stumpf, M P 2010 ‘ABC-SysBio-approximate Bayesian computation in Python with GPU support’. Bioinformatics, 26(14): 1797–1799. DOI: 10.1093/bioinformatics/btq278
- Metropolis, N, Rosenbluth, A W, Rosenbluth, M N, Teller, A H and Teller, E 1953 ‘Equation of state calculations by fast computing machines’. The Journal of Chemical Physics, 21(6): 1087–1092. DOI: 10.1063/1.1699114
- Mukherjee, P, Parkinson, D and Liddle, A R 2006 ‘A nested sampling algorithm for cosmological model selection’. The Astrophysical Journal Letters, 638(2):
L51 . URL:https://arxiv.org/pdf/astro-ph/0508461 . DOI: 10.1086/501068 - Neal, R M, et al. 2011 ‘MCMC using Hamiltonian dynamics’. Handbook of markov chain monte carlo, 2(11): 2. DOI: 10.1201/b10905-6
- Robinson, M, Bond, A, Simonov, A, Zhang, J and Gavaghan, D 2018 ‘Separating the effects of experimental noise from inherent system variability in voltammetry: The [Fe(CN)6]3/4 process’. ChemRxiv. DOI: 10.26434/chemrxiv.7149281.v1
- Robinson, M, Ounnunkad, K, Zhang, J, Gavaghan, D and Bond, A 2018 ‘Integration of heuristic and automated parametrization of three unresolved twoelectron surfaceconfined polyoxometalate reduction processes by AC voltammetry’. ChemElectroChem. DOI: 10.1002/celc.201800950
- Salvatier, J, Wiecki, T V and Fonnesbeck, C 2016 ‘Probabilistic programming in Python using PyMC3’. PeerJ Computer Science, 2:
e55 . DOI: 10.7717/peerj-cs.55 - Schaul, T, Glasmachers, T and Schmidhuber, J 2011 ‘High dimensions and heavy tails for natural evolution strategies’. In: ‘Proceedings of the 13th annual conference on Genetic and evolutionary computation’, ACM, 845–852. DOI: 10.1145/2001576.2001692
- Skilling, J, et al. 2006 ‘Nested sampling for general Bayesian computation’. Bayesian Analysis, 1(4): 833–859. DOI: 10.1214/06-BA127
- Ter Braak, C J 2006 ‘A Markov Chain Monte Carlo version of the genetic algorithm Differential Evolution: easy Bayesian computing for real parameter spaces’. Statistics and Computing, 16(3): 239–249. DOI: 10.1007/s11222-006-8769-1
- Thijssen, B, Dijkstra, T M, Heskes, T and Wessels, L F 2016 ‘BCM: toolkit for Bayesian analysis of computational models using samplers’. BMC Systems Biology, 10(1): 100. DOI: 10.1186/s12918-016-0339-3
- Vrugt, J A, Ter Braak, C, Diks, C, Robinson, B A, Hyman, J M and Higdon, D 2009 ‘Accelerating Markov chain Monte Carlo simulation by differential evolution with self-adaptive randomized subspace sampling’, International Journal of Nonlinear Sciences and Numerical Simulation, 10(3): 273–290. DOI: 10.1515/IJNSNS.2009.10.3.273
- Vyshemirsky, V and Girolami, M 2008 ‘BioBayes: a software package for Bayesian inference in systems biology’. Bioinformatics, 24(17): 1933–1934. DOI: 10.1093/bioinformatics/btn338
DOI: https://doi.org/10.5334/jors.252 | Journal eISSN: 2049-9647
Language: English
Submitted on: Nov 7, 2018
Accepted on: Jul 5, 2019
Published on: Jul 19, 2019
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
© 2019 Michael Clerx, Martin Robinson, Ben Lambert, Chon Lok Lei, Sanmitra Ghosh, Gary R. Mirams, David J. Gavaghan, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.