Weighted Markov chains and graphic state nodes for information retrieval

Letters on Geo graphic Information Retrieval Geo graphic Information Retrieval

Weighted Lumpability on Markov Chains

This paper reconsiders Bernardo’s T-lumpability on continuous-time Markov chains (CTMCs). This notion allows for a more aggressive state-level aggregation than ordinary lumpability. We provide a novel structural definition of (what we refer to as) weighted lumpability, prove some elementary properties, and investigate its compatibility with linear real-time objectives. The main result is that t...

Application of Markov chains in an interactive information retrieval system

This paper explores the application of Markov chains in an interactive information retrieval system. Building on the ideas of end-user's state of uncertainty, the system's model of language use, application of the results of information seeking to a work situation, and the user's feedback to the retrieval set through a graphic display of related nodes, the paper details how a Markov chain forms...

Using Markov Chains to Exploit Word Relationships in Information Retrieval

Document expansion and query expansion aim to add related terms into document and query representations in order to make them more complete. However, most previous studies are limited in two respects: They use either query expansion or document expansion, but not both; expansion has been limited to directly related words. In this paper, we propose a more general approach: both document and quer...

State counts for short Markov chains

Imagine we have a multi-step process that can be in any of a finite number of states. At any given time the process will be in one of these states, but at the next time-step it transitions to a new state (which may be the same state though now at a different time). If the probabilities of transitioning to the new state are fixed and independent of the present state, then this process is referre...