This file summarises what has been done in our project (DiPS and Bees) and will be done in the foreseeable future. If you are aware of something missing or something which is worth to look at, please comment.
GENERAL FEATURES
Related Work
[x] parameter synthesis by the formal methods - see github mcss_papers
[ ] other related work - see drive
[x] Experimentally induced innovations lead to persistent culture via conformity in wild birds
[x] Probabilistic Programming Process Algebra
[x] Distributed Information Processing in Biological and Computational Systems
[ ] others???
Models
[x] pMCs
[x] PRISM format
[x] pMDP
[x] PRISM format
[ ] check on more models
state-space reduction #small_project
[ ] use bisimulation minimisation, see Hahn, E.M., Hermanns, H., Zhang, L.: Probabilistic reachability for parametric Markov models. STTT 13(1), 3–19 (2010)
[ ] ❔ consult prior estimator - I see only first two params there @tatjanapetrov @xsafran1
[ ] ❔ consult walker - sd=0.3 (In the walker, there is the standard deviation of 0.3 in the code, which was fine when we had params from [0,1] but in the sigmoidal model and in general, the sd should maybe look like old_point_value * sd (vector multiplication)) @tatjanapetrov @xsafran1
MHMH (Metropolis-Hastings guided refinement)
[x] propose method
[x] implement method
[ ] multithreading computation / parallelisation (in process) #small_project
[ ] parametric model checking
[x] sampling
[x] pool.map
[x] refinement
[x] pool.map
[x] asyn 3 queues
[ ] :x: Metropolis-Hastings (hard)
[x] MHMH
[x] pool.map
[ ] gui - see #33
[ ] identifiability of parameters #small_project
Combining the methods
[ ] regression + sampling #small_project
[ ] sample regression point #small_project
[ ] regression + MH #small_project
[ ] start in the regression point #small_project
[ ] sampling + refinement #small_project
[ ] presampling of the space before splitting -- see #21 #22 (in progress)
sampling
[x] uniform sampling
[x] using orthogonal hull
[ ] MCMC/MH + refinement #small_project
[ ] refine the space based on some of MH bins
Tool/GUI
Features
[x] load, edit, save model
[x] load, edit, save properties
[x] call PRISM, parse the output
[x] call Storm, parse the output
[x] factorise, load, save the functions
[x] sample functions
[x] Grid sampling
[x] selected point
[x] heatmap (2D)
[x] load (.p/csv) and save data
[x] show data
[x] load, compute, save data intervals
[x] compute, load, save, append constraints
[x] show rat. functions (with data (with data intervals))
[x] Space Sampling
[ ] possible imporovements
[x] Space Refinement
[ ] possible imporovements
[x] Quantitative Space Sampling
[ ] possible imporovements
[x] config file
[ ] Model selection #small_project
User testing #small_project discuss with @tatjanapetrov
answer design, layout, readability, and also to query possible new features, potentially suggested by biologists.
This file summarises what has been done in our project (DiPS and Bees) and will be done in the foreseeable future. If you are aware of something missing or something which is worth to look at, please comment.
GENERAL FEATURES
Related Work
Models
Properties
Rational functions
Obtaining the polynomials
Data
(Confidence) Intervals
Constraints
Visualisation
Methods
sd=0.3(In the walker, there is the standard deviation of 0.3 in the code, which was fine when we had params from [0,1] but in the sigmoidal model and in general, the sd should maybe look like old_point_value * sd (vector multiplication)) @tatjanapetrov @xsafran1Tool/GUI
Features
User testing #small_project discuss with @tatjanapetrov
answer design, layout, readability, and also to query possible new features, potentially suggested by biologists.
Notebooks
CASE STUDY - BEE PROJECT #small_project
see Gdrive and Git
Models
Models by recursive functions
Models by composition
we adapted Sokolova SCCS composition with PMDPs: see github mcss_papers/Tatjana_draft
PRISM Models (DTMCs) see Gdrive, and Git
Properties
Rational functions
Data
(Confidence) Intervals
Constraints
Methods
Analysis