Applying Hidden Markov Models to Voting Advice Applications

Estimating Party-user Similarity in Voting Advice Applications using Hidden Markov Models

Voting Advice Applications (VAAs) are Web tools that inform citizens about the political stances of parties (and/or candidates) that participate in upcoming elections. The traditional process that they follow is to call the users and the parties to state their position in a set of policy statements, usually grouped into meaningful categories (e.g., external policy, economy, society, etc). Havin...

On Markov and Hidden Markov Models with Applications to Trajectories

Hidden Markov Models: Fundamentals and Applications Part 2: Discrete and Continuous Hidden Markov Models

The objective of this tutorial is to introduce basic concepts of a Hidden Markov Model (HMM). The tutorial is intended for the practicing engineer, biologist, linguist or programmer who would like to learn more about the above mentioned fascinating mathematical models and include them into one’s repertoire. This part of the tutorial is devoted to the basic concepts of a Hidden Markov Model. You...

Hidden Markov models in computational biology. Applications to protein modeling.

Hidden Markov Models (HMMs) are applied to the problems of statistical modeling, database searching and multiple sequence alignment of protein families and protein domains. These methods are demonstrated on the globin family, the protein kinase catalytic domain, and the EF-hand calcium binding motif. In each case the parameters of an HMM are estimated from a training set of unaligned sequences....

Hidden Markov Models with Applications to DNA Sequence Analysis

The Hidden Markov Model (HMM) is a model with a finite number of states, each associated with a probability distribution. The transitions between states cannot be directly measured (hidden), but in a particular state an observation can be generated. It is the observations and not the states themselves which are visible to an outside observer. However, by applying a series of statistical techniq...