# Videos tagged with Markov Processes

Suffix tree construction. Mention the new linear time array constructions - - using suffix trees for finding motifs with gaps (some new observations: 0.5 - 1 hours). - finding cis-regulatory motifs by comparative genomics (1 hour) - Hidden Markov techniques for haplotypingAuthor: Esko Ukkonen, University Of Helsinki

## Crossover random fields: A practical framework for learning and inference with graphical models and applications to computer vision and image processing problems

Graphical Models, such as Markov random fields, are a powerful methodology for modeling probability distributions over large numbers of variables. These models, in principle, offer a natural approach to learning and inference of many computer vision problems, such as stereo, denoising, segmentation, and image labeling. However, graphical models face severe computational problems when dealing wi...

## Book-Adaptive and Book-Dependent Models to Accelerate Digitization of Early Music

Optical music recognition (OMR) enables early music collections to be digitized on a large scale. The workflow for such digitisation projects also includes scanning and preprocessing, but the cost of expert human labour to correct automatic recognition errors dominates the cost of these other two steps. To reduce the number of recognition errors in the OMR process, we present an innovative appl...

## Separating Precision and Mean in Dirichlet-enhanced High-order Markov Models

Lecture slides: Agenda - Necessity of robustly estimating high-order Markov process models Necessity of robustly estimating high-order Markov process models - Natural language and Markov models Necessity of robustly estimating high-order Markov process models - Problem caused by data sparseness Necessity of robustly estimating high-order Markov process models - Introducing smoothing methods Age...

## Variational Inference for Markov Jump Processes

Markov jump processes (MJPs) underpin our understanding of many important systems in science and technology. They provide a rigorous probabilistic framework to model the joint dynamics of groups (species) of interacting individuals, with applications ranging from information packets in a telecommunications network to epidemiology and population levels in the environment. These processes are usu...

## Efficient max-margin Markov learning via conditional gradient and probabilistic inference

We present a general and efficient optimisation methodology for for max-margin sructured classification tasks. The efficiency of the method relies on the interplay of several techiques: marginalization of the dual of the structured SVM, or max-margin Markov problem; partial decomposition via a gradient formulation; and finally tight coupling of a max-likelihood inference algorithm into the opti...

## On Max-Margin Markov Networks in Hierarchical Document Classification

Lecture slides: Hierarchical Multilabel Classifcation: union of partial paths model Frequently used learning strategies for hierarchies The classification model Feature vectors Loss functions for hierarchies Max-margin Structured output learning Optimization problem Marginalized problem Decomposing the model Conditional Gradient method Conditional Gradient Ascent Using inference to find update ...

## Machine learning for access and retrieval II

Lecture slides: Hidden Markov Models with Applications Outline Clustering by Mixture Models Clustering Approaches to Clustering Mixture Model-based Clustering Mixture of Normals Advantages EM Algorithm EM for the Mixture of Normals Computation Issues Examples Classification Likelihood Classification EM Outline Author: Jia Li, Pennsylvania State University

## Constrained Hidden Markov Models for Population-based Haplotyping

Analysis of genetic variation in human populations is critical to the understanding of the genetic basis for complex diseases. Although genomes of several species have been sequenced, it is still too expensive to sequence genomes of several individuals to analyze genetic variation. Furthermore, most of the genome is invariant among individuals. Author: Niels Landwehr, University of Freiburg

## Variational Bayes for Continuous-time Nonlinear State-space Models

Lecture slides: Outline Nonlinear dynamical systems Nonlinear state-space models (NSSMs) Nonlinear state-space models (NSSMs)01 Variational inference for the NSSM Discrete-time models: pros and cons Continuous-time NSSM Stochastic Differential Equations Continuous-time NSSM Approximations Variational continuous-time NSSM State inference Faster state inference Experiment: Continuous-time NSSM Ex...