<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Markov Chain Monte Carlo | A Quest After Perspectives</title><link>https://iphysresearch.github.io/blog/tag/markov-chain-monte-carlo/</link><atom:link href="https://iphysresearch.github.io/blog/tag/markov-chain-monte-carlo/index.xml" rel="self" type="application/rss+xml"/><description>Markov Chain Monte Carlo</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Fri, 03 Apr 2026 00:00:00 +0000</lastBuildDate><image><url>https://iphysresearch.github.io/blog/media/sharing.png</url><title>Markov Chain Monte Carlo</title><link>https://iphysresearch.github.io/blog/tag/markov-chain-monte-carlo/</link></image><item><title>FluxMC: Rapid and High-Fidelity Inference for Space-Based Gravitational-Wave Observations</title><link>https://iphysresearch.github.io/blog/mypublication/2026_fluxmc/</link><pubDate>Fri, 03 Apr 2026 00:00:00 +0000</pubDate><guid>https://iphysresearch.github.io/blog/mypublication/2026_fluxmc/</guid><description>&lt;p>Benchmarks cover multimodal Gaussian targets and massive-black-hole-binary
signals with IMRPhenomD and IMRPhenomHM waveforms. Flow-generated global
proposals complement parallel-tempering exploration while the Monte Carlo
step preserves asymptotic sampling correctness.&lt;/p></description></item></channel></rss>