Probabilistic Deep Learning finds its application in autonomous vehicles and medical diagnoses. This is an increasingly important area of deep learning that aims to quantify the noise and uncertainty that is often present in real-world datasets.

PHP implementation of statistical probability distributions: normal distribution, beta distribution, gamma distribution and more.

A MATLAB project which applies the central limit theorem on PDFs and CDFs of different probability distributions.

PyPi package for modelling Probability distributions

Fast generation of long sequencies of bernoulli-distributed random variables

My works for EE 511 - Simulation Methods For Stochastic Systems - Spring 2018 - Graduate Coursework at USC - Dr. Osonde A. Osoba

In probability theory and statistics, the Bernoulli distribution, named after Swiss mathematician Jacob Bernoulli, is the discrete probability distribution of a random variable which takes the value 1 with probability and the value 0 with probability, in this example we will be able to visualize that in graphics mode

Statistics library for Dart

EM learning for a mixture of K multivariate Bernoullis with binary images

Reinforcement Learning (COMP 579) Project

C Programming Solutions for Real Analysis and Numerical Analysis Problems

This repository has been created to complete an assignment given by datainsightonline.com. This assignment is a part of Data Insight | Data Science Program 2021.

Leveling Candy Crush Episode's difficulty using Bernoulli principles

Image colorization with a Multivariate Bernoulli Mixture Density network.

Learned as a part of CS230 course

Artificial Intelligence Project aueb Winter 2019

Used data of emails being spam or non-spam for performing text classification using different probability distributions. Used NLTK library to remove stop words, non-alphabetic characters, and for tokenizing the text. Calculated mean and variance and other params for each word based on the label(spam or ham).

Write a program (in your favorite language) to obtain N samples from each of the following distributions: (i) Bernoulli with μ = 0.5; (ii) Poisson with parameter λ = 5; and (iii) Uniform on [0, 10].