Reconstructing model parameters in partially-observable discrete stochastic systems

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6751 LNCS, Issue , 2011, Pages 159-174

  Buchholz, Robert ^a   Krull, Claudia ^a   Horton, Graham ^a  

^a OTTO VON GUERICKE UNIVERSITY   (Germany)

Author keywords

Hidden non Markovian Model; incomplete model; parameter inference; partially observable discrete stochastic system

Indexed keywords

COMPUTATIONAL EFFORT; DEFECTIVE ITEMS; DISCRETE STOCHASTIC MODELS; DISCRETE STOCHASTIC SYSTEMS; DISTRIBUTED TIME; GENERAL MODEL; HIDDEN NON-MARKOVIAN MODEL; INCOMPLETE MODEL; MODEL PARAMETERS; MODEL SPECIFICATIONS; PARAMETER INFERENCE; PRODUCTION FACILITY; REAL-WORLD SYSTEM; TEST PROTOCOLS; TWO MACHINES; UNKNOWN PARAMETERS;

MARKOV PROCESSES; STOCHASTIC SYSTEMS;

STOCHASTIC MODELS;

EID: 79959356346     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-21713-5_12     Document Type: Conference Paper

References (16)

1. van der Aalst, W.M.P.: Analysis of discrete-time stochastic petri nets. Statistica Neerlandica 54(2), 237-255 (2000)
2. Aoki, M.: State space modeling of time series. Springer-Verlag New York, Inc., New York (1986)
3. Bobbio, A., Puliafito, A., Telek, M., Trivedi, K.S.: Recent developments in nonmarkovian stochastic petri nets. Journal of Systems Circuits and Computers 8(1), 119-158 (1998)
4. Borshchev, A., Filippov, A.: From system dynamics and discrete event to practical agent based modeling: Reasons, techniques, tools. In: Proceedings of 22nd International Conference of the System Dynamics Society, Oxford, England (July 2004)
5. Boys, R.J., Wilkinson, D.J., Kirkwood, T.B.: Bayesian inference for a discretely observed stochastic kinetic model. Statistics and Computing 18, 125-135 (2008)
6. Buchholz, R., Krull, C., Horton, G.: Efficient event-driven proxel simulation of a subclass of hidden non-markovian models. In: 7th EUROSIM Congress on Modelling and Simulation (2010)
7. Buchholz, R., Krull, C., Horton, G., Strigl, T.: Using hidden non-markovian models to reconstruct system behavior in partially-observable systems. In: 3rd International ICST Conference on Simulation Tools and Techniques (2010)
8. FSF. The gnu multiprecision library (gmp), http://gmplib.org
9. Gibson, G.J., Renshaw, E.: Estimating parameters in stochastic compartmental models using markov chain methods. Mathematical Medicine and Biology 15(1), 19-40 (1998)
10. Horton, G.: A new paradigm for the numerical simulation of stochastic petri nets with general firing times. In: European Simulation Symposium. SCS European Publishing House, Dresden (2002)
11. Krull, C., Buchholz, R., Horton, G.: Matching hidden non-markovian models: Diagnosing illnesses based on recorded symptoms. In: The 24th annual European Simulation and Modelling Conference (October 2010)
12. Krull, C., Horton, G.: Hidden non-markovian models: Formalization and solution approaches. In: Proceedings of 6th Vienna International Conference on Mathematical Modelling, Vienna, Austria (February 2009)
13. Lazarova-Molnar, S.: The Proxel-Based Method: Formalisation, Analysis and Applications. Ph.D. thesis, Otto-von-Guericke University Magdeburg (2005)
14. Malyarenko, A., Vasiliev, V.: On guaranteed parameter estimation of discrete-time stochastic systems. In: Second International Conference on Innovative Computing, Information and Control ICICIC 2007, p. 140 (September 2007)
15. Rabiner, L.R.: A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE 77(2), 257-286 (1989)
16. Wang, Y., Christley, S., Mjolsness, E., Xie, X.: Parameter inference for discretely observed stochastic kinetic models using stochastic gradient descent. BMC Systems Biology 4(1), 99 (2010)