markovian

Simulation of Markov Processes as stochastic processes.

Main features

• Easy construction of Markov processes, including:
  • Discrete time
  • Continuous time (exponential times)
• Type agnostic

Changelog

Last version:

• 0.1.3
  • Documentation added
  • Re-export traits module to crate level

For more, see Changelog.

To do list

• [ ] Document assumptions to create a process (probability distribution input)
• [ ] Tests

Roadmap

Separate sub and proper stochastic processes

Goal: Construct correctly stochastic and sub-stochastic process in different structs.

Current implementation: Sub-stochastic process for all structs.

Options:

• Needs:
  • Exact transitions

Implement Distribution

Goal: Random processes are also source of random transitions, therefore, we should be able to sample transitions.

Current implementation: None

Options:

• rand_distr::Distribution

Differentiate Markov Chains in continuous space

Goal: Easier and checkable implementation of continuous space markov processes by using randomness from the chain to simulate the next step.

Current implementation: Random transition function that leads a vector of one element.

Options:

• Needs
  • random generator choice.

Sample trajectory

Goal: Random processes are also source of random trajectories. Therefore, we should be able to sample them.

Current implementation: None

Options:

• method sampletrajectory
  • sampletrajectoryiter as in randdistr::Distribution

Random generator choice

Goal: Include random generator to the construction step.

Current implementation: New standard sampler for each step simulation.

Options:

• rand

Exact transitions

Goal: Integration with some crate for creation of a correct (sub-)distribution for each step.

Current implementation: f64 for probabilities and there is no correctness check.

Options:

• Rational numbers
• statrs
• rand_distr
• probability

Enlarge traits

Goal: Give more blank implementations and facilitate the implementation of Iterator trait. In particular, the following methods:

• transition(&self) -> &I
• canmoveto(&self, state: T) -> bool
• rate_to(&self, state: T) -> Option CMarkovChainTrait only
• probability_to(&self, state: T) -> Option MarkovChainTrait only

Current implementation: None

Options:

Contribution

Your contribution is highly appreciated. Do not hesitate to open an issue or a pull request. Note that any contribution submitted for inclusion in the project will be licensed according to the terms of the dual license (MIT and Apache-2.0).