We describe how we used a data set of chorale harmonisations composed by Johann Sebastian Bach to train Hidden Markov Models. Using a probabilistic framework allows us to create a...
We consider the problem of modeling the content structure of texts within a specific domain, in terms of the topics the texts address and the order in which these topics appear. W...
In this paper, we describe our work on classification of players in Massively Multiplayer Online Games using Hidden Markov Models based on player action sequences. In our previous ...
Hidden Markov models play a critical role in the modelling and problem solving of important AI tasks such as speech recognition and natural language processing. However, the stude...
Repetition is an important phenomenon in a variety of domains, such as music, computer programs and architectural drawings. A generative model for these domains should account for...
We investigate a family of inference problems on Markov models, where many sample paths are drawn from a Markov chain and partial information is revealed to an observer who attemp...
Daniel Sheldon, M. A. Saleh Elmohamed, Dexter Koze...
Neural activity is non-stationary and varies across time. Hidden Markov Models (HMMs) have been used to track the state transition among quasi-stationary discrete neural states. W...
Kentaro Katahira, Jun Nishikawa, Kazuo Okanoya, Ma...
In this paper we prove that the well-known correspondence between the forward-backward algorithm for hidden Markov models (HMMs) and belief propagation (BP) applied to HMMs can be...
This paper presents an algorithm for inferring a Structured Hidden Markov Model (S-HMM) from a set of sequences. The S-HMMs are a sub-class of the Hierarchical Hidden Markov Model...