Sampling particles on a hypersurface with local event-by-event account of energy, momentum, baryon number, strangeness and charge conservation.

An open-source JAX-based statistical sampling toolkit 🧪

Analysis of mock A/B Test Results by an e-commerce company. Application of probability, hypothesis testing, sampling distribution, two-sample z-test, and logistic regression to determining whether the company should implement the new web page it developed to increase users' conversion rate

Tools to support the Disscrete-Event Simulation process for education and practice.

A modern Fortran statistical library.

Rhombic grids for coherent plane-wave compounding (CPWC) in ultrasound imaging

Demonstrates reverse annealing on D-Wave quantum computers.

Tutorial: A simple GAN to generate samples from Gaussian distribution

Package provides python implementation of statistical inference engine

Use bootstrap resampling to estimate the sampling distribution of a statistic

This takes any Pandas or Dask dataframe and returns a resampled Dask dataframe simulating the sampling distribution of your data in one line of code. This is like the rep_sample_n() function from the infer package in R, but on steroids and made for quickly simulating a large number of replicate samples and even with a large number of observation…

Some R and Python code I've been working on.

A/B tests are very commonly performed by data analysts and data scientists. It is important that you get some practice working with the difficulties of these.

This code can be used to reproduce the results in our paper ``A Control Approach for Nonlinear Stochastic State Uncertain Systems with Probabilistic Safety Guarantees''.

Demonstrate techniques that help quantum applications find better, more robust solutions by comparing two generations of D-Wave 2000Q QPUs.

Applying A/B test to help determing if company should launch the new page

working to understand the results of an A/B test run by an e-commerce website. The company has developed a new web page in order to try and increase the number of users who "convert," meaning the number of users who decide to pay for the company's product.

Likelihood model framework

This repository is to demonstrate my understanding of the Sampling in Python course on Datacamp.

The repository covers some of the key concepts of Inferential Statistics with the help of R.