Hi,
I am Smit Lunagariya, an undergraduate student from Mathematics and
Computing Engineering, Indian Institute Technology-BHU. I am programming in
python for one year. I am interested in Mathematics and its symbolic
computation, specifically in Statistics. I have experience in Probabilistic
Machine Learning and Deep Learning.
I have undertaken several relevant Courses related such as Probability and
Statistics, Abstract Algebra, Engineering Mathematics, NPTEL -Stochastic
Process By Dr. S. Dharmaraja (IIT-Delhi), Data Structures and Information
Technology Workshop (on Python). Currently, I am enrolled in several
institute courses such as Algorithms, Numerical Techniques, Operating
Systems, and Mathematical Methods.
I have been contributing to sympy since December 2019 and got quite
familiar with the contributing guidelines and workflow.
I would like to discuss related to the idea for GSoC 2020 in the stats
module. I have prepared a rough timeline regarding this summer project.
* Community Bonding Period *:
As many distributions can be added in the stats module under Discrete and
Continuous Random variable, I would like to add them as some of them might
be useful in further implementation of Joint Multivariate Distributions.
They are:
1. Borel (Discrete)
2. Conway-Maxwell-Poisson (Discrete)
3. Gauss-Kuzmin (Discrete)
4. Lomax (Continuous)
5. Feller-Pareto (Continuous)
6. Bounded Pareto (Continuous)
7. Symmetric Pareto (Continuous)
8. Logit Normal (Continuous)
9. Inverse Gaussian (Continuous)
10. Inverse Chi-squared (Continuous)
Also, I would add the `.doit()` method in class Probability.
While adding these distributions I would also work upon increasing the code
coverage by adding tests and also tests of missing lines from the `crv.py`,
`drv.py`, `frv.py`, `drv_types.py`, `crv_types.py` and `frv_types.py`.
* Phase 1 *:
Currently, the stats module supports Markov chains and Bernoulli Process as
the stochastic processes. I would like to add more of such stochastic
processes which include:
1. Poisson Process
2. Birth-Death Process
3. Wiener Process
5. Random Walks
6. Gamma Process
7. Queueing Process
While adding the above process, I would also work upon adding their related
tests and increasing the code coverage of the `stochastic_process_types.py`.
* Phase 2 *:
During the beginning of this phase, I would try to clean up the remaining
part of Phase 1 and would then start implementing the following portions:
1. Work upon Adding assumptions of the dependence of random variables.
2. Work upon Adding support of Compound Distributions and adding more
examples related to it.
3. Adding densities of Circular ensembles in Random Matrices.
While discussing the API and implementing it, I would ensure to add the
necessary tests and work on increasing the code coverage.
* Phase 3 *:
During the beginning of this phase, I would try to clean up the remaining
part of Phase 2 and would then start implementing the following portions:
1. Currently, Joint distribution lacks a well-defined framework, I would
work upon changing the specific portions and make it more general for more
distributions to add upon it.
2. Adding more multivariate distributions which include:
1. Wishart
2. Matrix Gamma
3. Normal Inverse Gamma
4. Inverse Wishart
5. Normal Wishart
6. Normal Inverse Wishart
7. Inverse Matrix Gamma
3. Adding sampling methods to Continuous Random variables from external
libraries such as pyc3, NumPy, and scipy.
While adding these distributions I would also work upon increasing the code
coverage by adding tests and also tests of missing lines from the
`joint_rv.py` and `joint_rv_types.py`.
Finally, I would complete the remaining work before the final evaluation.
I have provided the rough timeline which I would like to follow during this
project. Changes and the addition of ideas and suggestions are appreciated.
Thank you.
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