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Integrating Inference with Stochastic Process Algebra Models - Jane Hillston, Edinburgh
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- 1 Intro
- 2 Stochastic Process Algebra
- 3 Integrated analysis
- 4 Benefits of integration
- 5 Outline
- 6 Modelling in a Data Rich World
- 7 Molecular processes as concurrent computations
- 8 Formal modelling in systems biology
- 9 Bio-PEPA modelling
- 10 The semantics
- 11 Optimizing models
- 12 Alternative perspective
- 13 Machine Learning Bayesian statistics
- 14 Comparing the techniques
- 15 Developing a probabilistic programming approach
- 16 Probabilistic programming workflow
- 17 A Probabilistic Programming Process Algebra: ProPPA
- 18 Example Revisited
- 19 Constraint Markov Chains
- 20 Probabilistic CMCS
- 21 Semantics of ProPPA
- 22 Simulating Probabilistic Constraint Markoy Chains
- 23 Calculating the transient probabilities
- 24 Basic Inference
- 25 Inference for infinite state spaces
- 26 Expanding the likelihood
- 27 Example model
- 28 Results: ABC
- 29 Genetic Toggle Switch
- 30 Toggle switch model: species
- 31 Experiment
- 32 Genes (unobserved)
- 33 Proteins
- 34 Summary
- 35 Challenges and Future Directions
