Leveraging Recursive Gumbel-Max Trick for Approximate Inference in Combinatorial Spaces, NeurIPS 2021

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

PyPi package for modelling Probability distributions

[R] Statistical analysis of financial data conducted in R

deepIRF - A deep learning approach for reverse-broadening the instrumental response (IRF) in lifetime spectra

Generation of random samples using R

Mathematics for Machine Leaning

Go implementation of the Xoroshiro128++, Xoshiro256++, and Xoshiro512++ pseudo random number generators

A simulation of an ACPC Grading System Server where multiple computers process incoming tasks. The system handles exponential interarrival and service times with a FIFO queue mechanism and calculates key performance metrics like average delay time, waiting time, and time-based average queue length.

Saratov State University, CSIT. Discipline "pseudo-random numbers generators" (псевдослучайные генераторы, rus)

Raku package for statistical distributions and related random variates generations.

Closed-form MLE and Fisher information for exponential series systems with masked failure data. Includes theoretical results, proofs, and numerical validation.

Teme Probabilitati si Statistica (PS), Anul 2, Semestrul 1, Facultatea de Matematica si Informatica, Universitatea din Bucuresti

Elements related to the exponential distribution

Distinguish power-law and geometric (discrete exponential) distributions using RStan with bridge sampling

This Shiny application allows users to visualize the cumulative distribution functions (CDFs) of a random variable 𝑥 following one of the selected probability distributions: Standard Normal Distribution - 𝑁(0,1) , General Normal Distribution - 𝑁(𝜇,𝜎^2) or Exponential Distribution - 𝐸𝑥𝑝(𝜆). Users can select the distribution's parameters.

This is a set of Python Codes related to a basic data science Probability Course at a masters level.

Distributed M/M/C queue simulation using Apache Kafka as a FIFO buffer, Spring Boot producer/consumer with configurable parallel servers, Poisson load generation via k6, and full performance monitoring with Prometheus, Grafana, and PostgreSQL for analytical validation (Little’s Law, utilization, throughput, and queue length).

A pure-Python survival analysis framework that models cancer patient survival times using the exponential distribution, incorporating Monte Carlo simulation, goodness-of-fit testing, bootstrap inference, and publication-quality visualizations for academic and healthcare analytics.