Stochastic Petri nets: an elementary introduction
Advances in Petri nets 1989
Condensed state spaces for symmetrical coloured Petri nets
Formal Methods in System Design - Special issue on symmetry in automatic verification
Stochastic Well-Formed Colored Nets and Symmetric Modeling Applications
IEEE Transactions on Computers
A Sweep-Line Method for State Space Exploration
TACAS 2001 Proceedings of the 7th International Conference on Tools and Algorithms for the Construction and Analysis of Systems
Symmetry Reductions inModel Checking
CAV '98 Proceedings of the 10th International Conference on Computer Aided Verification
Optimal state-space lumping in Markov chains
Information Processing Letters
Counting All Possible Ancestral Configurations of Sample Sequences in Population Genetics
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Coloured Petri Nets: Modelling and Validation of Concurrent Systems
Coloured Petri Nets: Modelling and Validation of Concurrent Systems
CPN tools for editing, simulating, and analysing coloured Petri nets
ICATPN'03 Proceedings of the 24th international conference on Applications and theory of Petri nets
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Biotechnological improvements over the last decade has made it economically and technologically feasible to collect large DNA sequence data from many closely related species. This enables us to study the detailed evolutionary history of recent speciation and demographics. Sophisticated statistical methods are needed, however, to extract the information that DNA sequences hold, and a limiting factor in this is dealing with the large state space that the ancestry of large DNA sequences spans. Recently a new analysis method, CoalHMMs, has been developed, that makes it computationally feasible to scan full genome sequences --- the complete genetic information of a species --- and extract genetic histories from this. Applying this methodology, however, requires that the full state space of ancestral histories can be constructed. This is not feasible to do manually, but by applying formal methods such as Petri nets it is possible to build sophisticated evolutionary histories and automatically derive the analysis models needed. In this paper we describe how to use colored stochastic Petri nets to build CoalHMMs for complex demographic scenarios.