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SciML / JumpProcesses.jl

Build and simulate jump equations like Gillespie simulations and jump diffusions with constant and state-dependent rates and mix with differential equations and scientific machine learning (SciML)
https://docs.sciml.ai/JumpProcesses/stable/
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Documentation: wording of the definition of a jump process #445

Open hurak opened 2 weeks ago

hurak commented 2 weeks ago

The mathematical overview in the documentation at https://github.com/SciML/JumpProcesses.jl/blame/9e272f797d1dfd23f79cc61a737d2a296e5f699d/docs/src/index.md#L52-L53C33 essentially reads that the random process dN(.) attains at a particular time t a value 1 with some probability. Am I interpreting it correctly? I would argue that this might misleading since such probability is always zero for a process defined over a continuous time, isn't it? Perhaps some rewording could help. I am not fluent with the stochastic process lingo, though.

isaacsas commented 2 weeks ago

Yes, that could use some rewriting for clarity. Thanks for pointing it out.