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]
Examples
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]
Applications
PDMPs have been shown useful in ruin theory, [3] queueing theory, [4] [5] for modelling biochemical processes such as DNA replication in eukaryotes and subtilin production by the organism B. subtilis, [6] and for modelling earthquakes. [7] Moreover, this class of processes has been shown to be appropriate for biophysical neuron models with stochastic ion channels. [8]
Properties
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]
Galtier and Al. [11] studied the law of the trajectories of PDMP and provided a reference measure in order to express a density of a trajectory of the PDMP. Their work opens the way to any application using densities of trajectory. (For instance, they used the density of a trajectories to perform importance sampling, this work was further developed by Chennetier and Al. [12] to estimate the reliability of industrial systems.)
See also
- Jump diffusion, a generalization of piecewise-deterministic Markov processes
- Hybrid system (in the context of dynamical systems), a broad class of dynamical systems that includes all jump diffusions (and hence all piecewise-deterministic Markov processes)
References
- ^ 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.
- ^ 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. S2CID 14257280.
- ^ 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. S2CID 124108500.
- ^ 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. S2CID 122273001.
- ^ 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: 1–14. CiteSeerX 10.1.1.556.6718. doi: 10.1017/S0269964805050011. S2CID 24065678.
- ^ Cassandras, Christos G.; Lygeros, John (2007). "Chapter 9. Stochastic Hybrid Modeling of Biochemical Processes" (PDF). Stochastic Hybrid Systems. CRC Press. ISBN 9780849390838.
- ^ 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.
- ^ 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. S2CID 18894661.
- ^ 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. S2CID 1453859.
- ^ 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.
- ^ Galtier, T. (2019). "On the optimal importance process for piecewise deterministic Markov process". Esaim: Ps. 23: 893–921. doi: 10.1051/ps/2019015. S2CID 198467101.
- ^ Chennetier, G. (2022). "Adaptive importance sampling based on fault tree analysis for piecewise deterministic Markov process". arXiv: 2210.16185 [ stat.CO].
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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]
Examples
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]
Applications
PDMPs have been shown useful in ruin theory, [3] queueing theory, [4] [5] for modelling biochemical processes such as DNA replication in eukaryotes and subtilin production by the organism B. subtilis, [6] and for modelling earthquakes. [7] Moreover, this class of processes has been shown to be appropriate for biophysical neuron models with stochastic ion channels. [8]
Properties
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]
Galtier and Al. [11] studied the law of the trajectories of PDMP and provided a reference measure in order to express a density of a trajectory of the PDMP. Their work opens the way to any application using densities of trajectory. (For instance, they used the density of a trajectories to perform importance sampling, this work was further developed by Chennetier and Al. [12] to estimate the reliability of industrial systems.)
See also
- Jump diffusion, a generalization of piecewise-deterministic Markov processes
- Hybrid system (in the context of dynamical systems), a broad class of dynamical systems that includes all jump diffusions (and hence all piecewise-deterministic Markov processes)
References
- ^ 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.
- ^ 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. S2CID 14257280.
- ^ 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. S2CID 124108500.
- ^ 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. S2CID 122273001.
- ^ 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: 1–14. CiteSeerX 10.1.1.556.6718. doi: 10.1017/S0269964805050011. S2CID 24065678.
- ^ Cassandras, Christos G.; Lygeros, John (2007). "Chapter 9. Stochastic Hybrid Modeling of Biochemical Processes" (PDF). Stochastic Hybrid Systems. CRC Press. ISBN 9780849390838.
- ^ 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.
- ^ 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. S2CID 18894661.
- ^ 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. S2CID 1453859.
- ^ 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.
- ^ Galtier, T. (2019). "On the optimal importance process for piecewise deterministic Markov process". Esaim: Ps. 23: 893–921. doi: 10.1051/ps/2019015. S2CID 198467101.
- ^ Chennetier, G. (2022). "Adaptive importance sampling based on fault tree analysis for piecewise deterministic Markov process". arXiv: 2210.16185 [ stat.CO].
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