Bayesian parameter inference for stochastic biochemical network models using particle Markov chain Monte Carlo

By Andrew Golightly, Darren J. Wilkinson. Interface Focus, 2011

314 citations recorded by the source corpus.

Subfields: Genetics, Molecular Biology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/d21c2cd6b6eccba41aee297635340d5bfa5c3eed

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

1. Vinayak A. Rao
2. Yee Whye Teh
3. Debashish Chowdhury
4. Lawrence M. Murray
5. David B. Dunson
6. A. Mcgough
7. Oksana A. Chkrebtii
8. Dennis Prangle

In Molecular Biology

1. Ben Calderhead
2. Yee Whye Teh
3. Vinayak A. Rao
4. Debashish Chowdhury
5. Lawrence M. Murray
6. David B. Dunson
7. Mark A. Girolami
8. A. Mcgough

In Statistics and Probability

1. Yee Whye Teh
2. Mark A. Girolami
3. David B. Dunson
4. Fredrik Lindsten
5. Thomas Bo Schön
6. Michael I. Jordan
7. David Alexander Campbell
8. Oksana A. Chkrebtii

Citations in the corpus

• Black-Box Variational Inference for Stochastic Differential Equations (2018, methodology): “A SDE Lotka-Volterra model (for derivation see e.g. Golightly & Wilkinson, 2011) is defined by where X t = ( U t , V t ) (cid:48) represents the populations of prey and predators at time t .”
• Mechanistic Hierarchical Gaussian Processes (2014, background): “Golightly and Wilkinson (2006), Golightly and Wilkinson (2011), and Zhu, Taylor, and Song (2011) are examples of approaches based on diffusions.”
• Stochastic mechano-chemical kinetics of molecular motors: A multidisciplinary enterprise from a physicist’s perspective (2012, background): “The areas of research where this has been applied successfully include various biological processes in, for example, genetics [459, 460], biochemistry [461], cognitive sciences [462], ecology [463], etc.”
• Bayesian State-Space Modelling on High-Performance Hardware Using LibBi (2013, background): “Examples are numerous, but include marine biogeochemistry [16, 37, 17, 59], ecological population dynamics [73, 57, 60, 35], Functional Magnetic Resonance Imaging [64, 52, 53], biochemistry [29, 30] and object tracking [71].”
• Bayesian Solution Uncertainty Quantification for Differential Equations (2013, methodology): “Such partially likelihood-free Markov chain Monte Carlo methods (Marjoram et al., 2003) are widely used for inference on stochastic differential equations (e.g., Golightly and Wilkinson, 2011).”
• Fast MCMC sampling for Markov jump processes and extensions (2012, methodology): “Keywords: Markov jump process, uniformization, MCMC, Gibbs sampler, Markov-modulated Poisson process, continuous-time Bayesian network”
• Biips: Software for Bayesian Inference with Interacting Particle Systems (2014, background): “…robotics (Thrun et al. 2001; Gustafsson et al. 2002; Vermaak et al. 2002; Vo et al. 2003; Ristic et al. 2004; Caron et al. 2007), systems biology (Golightly and Wilkinson 2006, 2011; Bouchard-Côté et al. 2012), economics and macro-economics (Pitt and Shephard 1999; Fernández-Villaverde and…”
• Particle gibbs with ancestor sampling (2014, background): “PMCMC has in a relatively short period of time found many applications in areas such as hydrology (Vrugt et al., 2013), finance (Pitt et al., 2012), systems biology (Golightly and Wilkinson, 2011), and epidemiology (Rasmussen et al., 2011), to mention a few.”
• Adaptive Gaussian Process Approximation for Bayesian Inference with Expensive Likelihood Functions (2017, background): “Such problems arise from many real-world applications, ranging from carbon capture [10] to chemical kinetics [4].”