markovian 0.1.2

Simulation of Markov Processes as stochastic processes.
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96

# 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:

- 0.1.2
  - Documentation added.
  - Fixed bug in method markovian::traits::BranchingTrait::approx_generating_fun.

For more, see [Changelog](https://github.com/rasa200/markovian/blob/master/Changelog.md).

# To do list


- [ ] 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 sample_trajectory
  - sample_trajectory_iter
    as in rand_distr::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

## 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).