A robust spectral method for finding lumpings and meta stable states of non-reversible Markov chains
arXiv:0810.112719 citationsh-index: 17
Originality Incremental advance
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For researchers working with large, noisy, non-reversible Markov chains, this method offers a more robust alternative for identifying lumpings and meta stable states.
The paper presents a spectral method for finding lumpings and meta stable states in non-reversible Markov chains, demonstrating robustness to noise compared to existing methods.
A spectral method for identifying lumping in large Markov chains is presented. Identification of meta stable states is treated as a special case. The method is based on spectral analysis of a self-adjoint matrix that is a function of the original transition matrix. It is demonstrated that the technique is more robust than existing methods when applied to noisy non-reversible Markov chains.