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<!DOCTYPE html>
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<title>Monte Carlo Statistical Methods | Sai Sampath Kedari</title>
<meta name="description" content="Step-by-step implementations and visualizations of core Monte Carlo techniques: sampling, LLN/SLLN/CLT, variance reduction, MCMC, and Bayesian basics. Work in progress." />
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<h1>MonteCarlo-Statistical-Methods</h1>
<p>This repository will include <strong>step-by-step implementations</strong> and <strong>visualizations</strong> of key Monte Carlo techniques.</p>
<p>🔗 <a href="https://github.com/SaiSampathKedari/MonteCarlo-Statistical-Methods" target="_blank">View on GitHub</a></p>
<h2>Planned Contents</h2>
<ol>
<li><strong>Random variable generation</strong>: Inverse Transform, Accept–Reject, Importance Sampling</li>
<li><strong>Monte Carlo estimation & convergence</strong>: WLLN, SLLN, CLT</li>
<li><strong>Variance reduction</strong>: Control Variates, Antithetic Sampling, Multilevel Control Variates</li>
<li><strong>MCMC algorithms</strong></li>
<li><strong>Bayesian inference and filtering basics</strong></li>
<li><strong>Applications</strong> to statistical estimation, learning, and robotics</li>
</ol>
<p>🚧 <strong>Work in progress</strong>, starting with fundamental sampling and convergence demos.</p>
<h2>Related Work</h2>
<ul>
<li><a href="https://github.com/SaiSampathKedari/Statistical-Inference-Theory" target="_blank">Statistical Inference Theory (Casella-Berger)</a></li>
<li><a href="https://github.com/SaiSampathKedari/Probability-and-Distribution-Theory" target="_blank">Probability & Distribution Theory (Casella-Berger)</a></li>
</ul>
<h2>Contact</h2>
<p>
Email: <a href="mailto:sampath@umich.edu">sampath@umich.edu</a><br />
LinkedIn: <a href="https://www.linkedin.com/in/sai-sampath-kedari" target="_blank">sai-sampath-kedari</a>
</p>
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<p>© <span id="y"></span> Sai Sampath Kedari — Monte Carlo Statistical Methods</p>
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