sampling-distribution

Build your own Face App with Stable Diffusion 2.1

A modern Fortran statistical library.

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

Tools to support the Discrete-Event Simulation and Monte-Carlo Simulation process for education and practice.

An open-source JAX-based statistical sampling toolkit 🧪

Simulation Methods in Econometrics: Theory and Applications in MATLAB is an open-source textbook that aims to bridge the gap between econometric theory and the practical use of simulation techniques. It emphasizes intuition, reproducibility, and accessibility.

JAX-based copula modelling

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

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

Likelihood model framework

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

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…

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

PRISM is a robust prompt optimization framework that moves beyond single-shot rewriting by exploring multiple candidate prompts, grouping them via semantic clustering, and selecting the best one through consensus-driven scoring.

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''.

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