Piecewise-deterministic Markov process

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In probability theory, a piecewise-deterministic Markov process (PDMP) is a process whose behaviour is governed by random jumps at points in time, but whose evolution is deterministically governed by an ordinary differential equation between those times. The class of models is "wide enough to include as special cases virtually all the non-diffusion models of applied probability."[1] The process is defined by three quantities: the flow, the jump rate, and the transition measure.[2]

The model was first introduced in a paper by Mark H. A. Davis in 1984.[1]


Piecewise linear models such as Markov chains, continuous-time Markov chains, the M/G/1 queue, the GI/G/1 queue and the fluid queue can be encapsulated as PDMPs with simple differential equations.[1]


PDMPs have been shown useful in ruin theory,[3] queueing theory,[4][5] for modelling biochemical processes such as subtilin production by the organism B. subtilis and DNA replication in eukaryotes[6] for modelling earthquakes.[7] Moreover, this class of processes has been shown to be appropriate for biophysical neuron models with stochastic ion channels.[8]


Löpker and Palmowski have shown conditions under which a time reversed PDMP is a PDMP.[9] General conditions are known for PDMPs to be stable.[10]


  1. ^ a b c Davis, M. H. A. (1984). "Piecewise-Deterministic Markov Processes: A General Class of Non-Diffusion Stochastic Models". Journal of the Royal Statistical Society. Series B (Methodological). 46 (3): 353–388. doi:10.1111/j.2517-6161.1984.tb01308.x. JSTOR 2345677.
  2. ^ Costa, O. L. V.; Dufour, F. (2010). "Average Continuous Control of Piecewise Deterministic Markov Processes". SIAM Journal on Control and Optimization. 48 (7): 4262. arXiv:0809.0477. doi:10.1137/080718541.
  3. ^ Embrechts, P.; Schmidli, H. (1994). "Ruin Estimation for a General Insurance Risk Model". Advances in Applied Probability. 26 (2): 404–422. doi:10.2307/1427443. JSTOR 1427443.
  4. ^ Browne, Sid; Sigman, Karl (1992). "Work-Modulated Queues with Applications to Storage Processes". Journal of Applied Probability. 29 (3): 699–712. doi:10.2307/3214906. JSTOR 3214906.
  5. ^ Boxma, O.; Kaspi, H.; Kella, O.; Perry, D. (2005). "On/off Storage Systems with State-Dependent Input, Output, and Switching Rates". Probability in the Engineering and Informational Sciences. 19. CiteSeerX doi:10.1017/S0269964805050011.
  6. ^ Cassandras, Christos G.; Lygeros, John (2007). "Chapter 9. Stochastic Hybrid Modeling of Biochemical Processes" (PDF). Stochastic Hybrid Systems. CRC Press. ISBN 9780849390838.
  7. ^ Ogata, Y.; Vere-Jones, D. (1984). "Inference for earthquake models: A self-correcting model". Stochastic Processes and Their Applications. 17 (2): 337. doi:10.1016/0304-4149(84)90009-7.
  8. ^ Pakdaman, K.; Thieullen, M.; Wainrib, G. (September 2010). "Fluid limit theorems for stochastic hybrid systems with application to neuron models". Advances in Applied Probability. 42 (3): 761–794. arXiv:1001.2474. doi:10.1239/aap/1282924062.
  9. ^ Löpker, A.; Palmowski, Z. (2013). "On time reversal of piecewise deterministic Markov processes". Electronic Journal of Probability. 18. arXiv:1110.3813. doi:10.1214/EJP.v18-1958.
  10. ^ Costa, O. L. V.; Dufour, F. (2008). "Stability and Ergodicity of Piecewise Deterministic Markov Processes" (PDF). SIAM Journal on Control and Optimization. 47 (2): 1053. doi:10.1137/060670109.