meiyulee / continuous_bernoulli Goto Github PK

There are C language computer programs about the simulator, transformation, and test statistic of continuous Bernoulli distribution. More than that, the book contains continuous Binomial distribution and continuous Trinomial distribution.

C++ 100.00%

deep-learning deep-neural-networks deeplearning variational-autoencoder continuous-bernoulli deeplearning-ai vae-implementation autoencoders c cpp

continuous_bernoulli's Introduction

A Github Pages template for academic websites. I downloaded from academicpages. There are some parts are from jayrobwilliams.

Academicpages mentioned:

A Github Pages template for academic websites. This was forked (then detached) by Stuart Geiger from the Minimal Mistakes Jekyll Theme, which is © 2016 Michael Rose and released under the MIT License. See LICENSE.md.

I think I've got things running smoothly and fixed some major bugs, but feel free to file issues or make pull requests if you want to improve the generic template / theme.

Note: if you are using this repo and now get a notification about a security vulnerability, delete the Gemfile.lock file.

Instructions

Register a GitHub account if you don't have one and confirm your e-mail (required!)
Fork this repository by clicking the "fork" button in the top right.
Go to the repository's settings (rightmost item in the tabs that start with "Code", should be below "Unwatch"). Rename the repository "[your GitHub username].github.io", which will also be your website's URL.
Set site-wide configuration and create content & metadata (see below -- also see this set of diffs showing what files were changed to set up an example site for a user with the username "getorg-testacct")
Upload any files (like PDFs, .zip files, etc.) to the files/ directory. They will appear at https://[your GitHub username].github.io/files/example.pdf.
Check status by going to the repository settings, in the "GitHub pages" section
(Optional) Use the Jupyter notebooks or python scripts in the markdown_generator folder to generate markdown files for publications and talks from a TSV file.

See more info at https://academicpages.github.io/

To run locally (not on GitHub Pages, to serve on your own computer)

Clone the repository and made updates as detailed above
Make sure you have ruby-dev, bundler, and nodejs installed: sudo apt install ruby-dev ruby-bundler nodejs
Run bundle clean to clean up the directory (no need to run --force)
Run bundle install to install ruby dependencies. If you get errors, delete Gemfile.lock and try again.
Run bundle exec jekyll liveserve to generate the HTML and serve it from localhost:4000 the local server will automatically rebuild and refresh the pages on change.

Changelog -- bugfixes and enhancements

There is one logistical issue with a ready-to-fork template theme like academic pages that makes it a little tricky to get bug fixes and updates to the core theme. If you fork this repository, customize it, then pull again, you'll probably get merge conflicts. If you want to save your various .yml configuration files and markdown files, you can delete the repository and fork it again. Or you can manually patch.

To support this, all changes to the underlying code appear as a closed issue with the tag 'code change' -- get the list here. Each issue thread includes a comment linking to the single commit or a diff across multiple commits, so those with forked repositories can easily identify what they need to patch.

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continuous_bernoulli's Issues

screenshot of exe files [07]

Runnig C_Bernoulli_07.exe

The computer program will run the goodness of fit for data.
Data in the screenshot is generated by C_Bernoulli_01.exe, with setting λ = 0.4 and sample size = 10000.

The computer program calculates the estimated λ = 0.454179.

screenshot of exe file [11]

Run the C_Bernoulli_11.exe.
This program is to work the interval estimation of two population means and of two population parameters, respectively.

screenshot of exe file [09]

C_Bernoulli_09.exe is to run two series of data and to compare their μ.
First, there are two series of data independently from CB(0.4).

Run C_Bernoulli_09.exe, then the program calculates the values of the sample means, the sample variances, and the estimated λ.

You can set a different value of c to test two population means.

screenshot of exe file [08]

C_Bernoulli_08.exe is about the goodness of fit for data.
Since I did not have any data, simulate a series of values from CB(0.4) using C_Bernoulli_01.exe
Of course, you can use your own data to run C_Bernoulli_08.exe

screenshot of exe files [05,06]

Run C:\C_Bernoulli\C_Bernoulli_05.exe

Before run C_Bernoulli_05.exe, you need to generate data of CB, or your data. Then C_Bernoulli_05.exe can test the lambda.

Run C:\C_Bernoulli\C_Bernoulli_06.exe

Find the lambda's confidence interval. Try it.

the "simulated_data.txt" is simulated by C_Bernoulli_01.exe.

screenshot of exe file [10]

C_Bernoulli_10.exe is for the test if two population parameters were the same. Now I simulate three data sets:
(1) simulated_data.txt λ = 0.4
(2) simulated_data.txt λ = 0.4
(3) simulated_data_08.txt λ = 0.8

(1) and (2) are independently simulated from CB(λ = 0.4).

The books of the continuous Bernoulli distribution

Continuous Bernoulli distribution can help the researchers transferring the discrete states to the continuous states in artificial intelligence and achieving accurate computations and predictions.

This github is for the first book of the continuous Bernoulli distribution to show (1) the statistical inference including the sufficient statistic, the point estimator, the interval estimation, the test statistic, the goodness of fit, and the one-way analysis. (2) the continuous binomial distribution, (3) the continuous trinomial distribution.

We also verified that
(1) the special Beta distribution and compared it with the continuous Bernoulli distribution.
(2) how about the continuous Bernoulli distribution and the continuous binomial distribution as λ ->0.
We also built the four models to solve the case of k categories.

The four books can be downloaded at vixra.org or researchgate.

Screen Shots of exe files [03,04]

The case is lambda = 0.4, n = 100.

Run C:\C_Bernoulli\C_Bernoulli_03.exe.

It simulates the sampling distribution of the sample mean for continuous Bernoulli.

Run C:\C_Bernoulli\C_Bernoulli_04.exe.

It simulates the continuous Bernoulli's lambda point estimator(MLE estimated equation).

Note: large n needs more time to get the sampling distribution of the sample mean.