Semi-Markov Process #4
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implementation @basf
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Apr 19, 2022
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2 tasks
- added support for weights which are functions
Solving the Markov Process results in probabilities for a fixed time range: 0.0:dt:tsim
Weighting function following the suggested Simpson method.
Temporary fix of unit compatibility problem line 46
allow for prob::Vector{Vector}, i.e., for p(t) rather than only p(Inf).
Correct implementation of sojourn time and time in callbacks of pdf and cdf. Restructured file.
The evaluation of the probability function in the transition rates gave an error caused by the unit coupled to "t". This is resolved by stripping "t" of it's unit for the sole purpose of evaluating that function. Most likely there is a better solution to this problem.
Adjustments made to improve compatibility with unitful and guarantee results from the pdf's
Given the ln-function, all unitful quantities are expressed in terms of the unit of μ for the pdf, cdf and ccdf.
Clear allocation of type to distribution constructors.
…code, added tests.
Another example of the semi-Markov process, to determine LVDC battery system resilience against AC disturbances.
merging semi-markov-finalization into semi-markov
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Rough implementation @basf