AND TURBO CODES Javier Garcia-Frias and John D. Villasenor Electrical Engineering Department University of California, Los Angeles Abstract|Hidden Markov models have been widely used to statistically characterize sources and channels in communication systems. In this paper we will consider their application in the context of turbo codes. We will describe simpli ed techniques for modifying a dec...

Figure 1. Finite state machine for a Markov chain X0 → X1 → X2 → · · · → Xn where the random variables Xi’s take values from I = {S1, S2, S3}. The numbers T (i, j)’s on the arrows are the transition probabilities such that Tij = P (Xt+1 = Sj|Xt = Si). Definition 1.2. We say that (Xn)n≥0 is a Markov chain with initial distribution λ and transition matrix T if (i) X0 has distribution λ; (ii) for ...

We introduce a new anomaly detection methodology for data with latent dependency structure. As a particular instantiation, we derive a hidden Markov anomaly detector that extends the regular one-class support vector machine. We optimize the approach, which is non-convex, via a DC (difference of convex functions) algorithm, and show that the parameter ν can be conveniently used to control the nu...

Algorithms such as Latent Dirichlet Allocation (LDA) have achieved significant progress in modeling word document relationships. These algorithms assume each word in the document was generated by a hidden topic and explicitly model the word distribution of each topic as well as the prior distribution over topics in the document. Given these parameters, the topics of all words in the same docume...

The recent literature on profile hidden Markov model (profile HMM) methods and software is reviewed. Profile HMMs turn a multiple sequence alignment into a position-specific scoring system suitable for searching databases for remotely homologous sequences. Profile HMM analyses complement standard pairwise comparison methods for large-scale sequence analysis. Several software implementations and...

One of the basic probabilistic tools used for time series modeling is the hidden Markov model (HMM). In an HMM, information about the past of the time series is conveyed through a single discrete variable|the hidden state. We present a generalization of HMMs in which this state is factored into multiple state variables and is therefore represented in a distributed manner. Both inference and lea...

We study a time series model that can be viewed as a decision tree with Markov temporal structure. The model is intractable for exact calculations, thus we utilize variational approximations . We consider three different distributions for the approximation: one in which the Markov calculations are performed exactly and the layers of the decision tree are decoupled, one in which the decision tre...

Article history: Received 14 April 2009 Available online 17 November 2009