Maximum Likelihood Estimation for Markov Chains
Iuliana Teodorescu
TL;DR
This paper introduces a novel maximum likelihood estimation method tailored for Markov chains with sparse transition matrices, aiming to improve estimation accuracy and efficiency.
Contribution
It proposes a new maximum likelihood estimation technique specifically designed for sparse Markov chain transition matrices, enhancing existing methods.
Findings
Improved estimation accuracy for sparse Markov chains
Efficient algorithm for maximum likelihood estimation
Better performance compared to traditional methods
Abstract
A new approach for optimal estimation of Markov chains with sparse transition matrices is presented.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsStatistical Methods and Inference · Bayesian Methods and Mixture Models · Markov Chains and Monte Carlo Methods