estimation-theory

Python notebooks for my graduate class on Detection, Estimation, and Learning. Intended for in-class demonstration. Notebooks illustrate a variety of concepts, from hypothesis testing to estimation to image denoising to Kalman filtering. Feel free to use or modify for your instruction or self-study.

Implementation of the paper "On the Asymptotic Mean Square Error Optimality of Diffusion Models."

StochasticA is a textbook / website for an “Introduction to Stochastic Signal Processing”. Materials for this website can be found here. Be sure to read the README.md document if you want to know more about the implementation.

This tutorial demonstrates how to compute maximum likelihood estimates of the parameters of a Gaussian distribution both analytically and using gradient descent.

Udacity Flying Car Nanodegree

Reproducible code for the paper "Limits of Manifold Projection in Calibrated Channel and Clutter Estimation": when projecting a calibrated channel or clutter estimate onto a specular manifold helps, when it makes the error worse, and the partial projection that never does.

Various filters developed in Matlab

This repository is a part of the Estimation theory laboratory exercises at the Faculty of Electrical Engineering and Computing, University of Zagreb

A Python Package for the Four-Parameter Beta Distribution and Likelihood-Based Estimation

Personal repository for COL786. Pardon mistakes!

Privacy Enabled Crowdsourced Transmitter Location: MS thesis work

Bayesian source term estimation in 2D atmospheric dispersion — SIR particle filter, Gaussian plume forward model, Fisher Information analysis. M.Tech research, IIT Bombay.

Assignments for class in signal processing and data analysis

A user-friendly toolkit for engineers and researchers to address the problem of aligning measurements from gyroscopes mounted on the same rigid body.

Python code for a particle filter estimator using pyParticleEst library

A list of projects from my Udacity course on Autonomous Flight

Generalized Python library for state estimation: Linear Kalman Filter with chi-squared innovation testing and Gauss-Helmert Model for implicit nonlinear least squares adjustment.

Monte-Carlo comparison of Projection, LMMSE, LASSO and MUSIC for sparse frequency (line-spectrum) estimation - MATLAB + Python. Shows L1 sparse recovery beating classical methods in the overcomplete regime.