🚀 A library designed to facilitate work with probability, statistics and stochastic calculus

A library providing math and statistics operations for numbers of arbitrary size.

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.

Python implementation of various soccer/football analytics methods such as Poisson goals prediction, Shin method, machine learning prediction... This is a companion python module for octosport medium blog.

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

Pieces of code that have appeared on my blog with a focus on stochastic simulations.

A comprehensive bundle of utilities for the estimation of probability of informed trading models: original PIN in Easley and O'Hara (1992) and Easley et al. (1996); Multilayer PIN (MPIN) in Ersan (2016); Adjusted PIN (AdjPIN) in Duarte and Young (2009); and volume-synchronized PIN (VPIN) in Easley et al. (2011, 2012). Implementations of various …

Generate point arrays for Geometry Nodes using cubic grid, golden angle (Fermat's spiral), poisson disc sampling, or import points from data sources in CSV, NPY, and VF (Unity 3D volume field) formats.

Prediction of Premier league standings using Poisson distribution

Fast Poisson Random Numbers in pure Julia for scientific machine learning (SciML)

Data Wrangling, Linear Models & other misc. Inferential Statistics.

CP-APR Tensor Decomposition with PyTorch backend. pyCP_APR can perform non-negative Poisson Tensor Factorization on GPU, and includes an interface for anomaly detection using the extracted latent patterns.

Data Science Portfolio

10 Days of Statistics Challenges at HackerRank

A Python library for working with and training Hidden Markov Models with Poisson emissions.

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

A collection of probability models applied to football

In this work, we examine the resilience of two complex network types (Erdos Renyi, and Power-Law/Scale-free) to potential delivered attacks and random errors.

Calculating attack strength and defense of teams. Determining games results using Poisson Distribution.

Simulation model to run scenarios on staffing/operations readiness for a call center