My Research - with Jun Yang
“Markov Chain Monte Carlo for High-Dimensional Problems” is this month’s contribution to our article series, written by Jun Yang, Assistant Professor - Tenure Track. He is working in both the SPT and the IE section.
“Markov Chain Monte Carlo (MCMC) methods have become essential tools for solving complex problems in mathematics, statistics, and data science. These algorithms allow us to approximate solutions to problems where direct computation is infeasible, especially in high-dimensional settings".
This is how Jun Yang begins – and he concludes the article like this:
“Ultimately, we hope our research will make MCMC a more accessible and effective tool for statisticians, mathematicians, and data scientists working on high-dimensional problems. Whether you're analyzing complex datasets, developing new statistical models, or exploring uncharted mathematical territories, the advancements in MCMC theory and methodology offer exciting opportunities for discovery."
Read the article Markov Chain Monte Carlo for High-Dimensional Problems.
In 2015, we launched an article series with the department's researchers in the MATH newsletter. The goal is to present research across the MATH Department. So far, there have been 64 articles.
See all the My Research articles here. Next month's writer is Line Clemmensen.
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