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The EM procedure is a principal tool for parameter estimation in the hidden Markov models. However, applications replace EM by Viterbi extraction, or training (VT). VT is computationally less… (More)

Abstract: We consider nonparametric Bayesian estimation of a probability density p based on a random sample of size n from this density using a hierarchical prior. The prior consists, for instance,… (More)

- Jüri Lember, Alexey A. Koloydenko
- IEEE Transactions on Information Theory
- 2010

Since the early days of digital communication, hidden Markov models (HMMs) have now been also routinely used in speech recognition, processing of natural languages, images, and in bioinformatics. In… (More)

- Faruk Göloglu, Jüri Lember, Ago-Erik Riet, Vitaly Skachek
- 2015 IEEE International Symposium on Information…
- 2015

New bounds on the cardinality of permutation codes equipped with the Ulam distance are presented. First, an integer-programming upper bound is derived, which improves on the Singleton-type upper… (More)

- Alexey A. Koloydenko, Meelis Käärik, Jüri Lember
- 2007

The EM algorithm is a principal tool for parameter estimation in the hidden Markov models, where its efficient implementation is known as the Baum–Welch algorithm. This paper is however motivated by… (More)

Viterbi Training (VT) provides a fast but inconsistent estimator of Hidden Markov Models (HMM). The inconsistency is alleviated with little extra computation when we enable VT to asymptotically fix… (More)

Let Ln be the length of the longest common subsequence of two independent i.i.d. sequences of Bernoulli variables of length n. We prove that the order of the standard deviation of Ln is √ n, provided… (More)

- Jüri Lember
- 2008

This paper is concerned with finding a fingerprint of a sequence. As input data one uses the sequence which has been randomly mixed up by observing it along a random walk path. A sequence containing… (More)

We propose modifications of the Viterbi Training (VT) algorithm to estimate emission parameters in Hidden Markov Models (HMM) which are widely used in speech recognition, natural language modeling,… (More)

- Jüri Lember, Alexey A. Koloydenko
- Journal of Machine Learning Research
- 2014

Motivated by the unceasing interest in hidden Markov models (HMMs), this paper reexamines hidden path inference in these models, using primarily a risk-based framework. While the most common maximum… (More)