Hidden Markov change point estimation

Hidden Markov Change Point Estimation

A hidden Markov model is considered where the dynamics of the hidden process change at a random ‘change point’ . In principle this gives rise to a non-linear filter but closed form recursive estimates are obtained for the conditional distribution of the hidden process and of .

Change point estimation in high dimensional Markov random-field models.

This paper investigates a change-point estimation problem in the context of high-dimensional Markov random field models. Change-points represent a key feature in many dynamically evolving network structures. The change-point estimate is obtained by maximizing a profile penalized pseudo-likelihood function under a sparsity assumption. We also derive a tight bound for the estimate, up to a logari...

Estimation of Hidden Markov ModelsJ

Hidden Markov Random Field Iterative Closest Point

When registering point clouds resolved from an underlying 2-D pixel structure, such as those resulting from structured light and flash LiDAR sensors, or stereo reconstruction, it is expected that some points in one cloud do not have corresponding points in the other cloud, and that these would occur together, such as along an edge of the depth map. In this work, a hidden Markov random field mod...

Hazard Change Point Estimation

In survival analysis or reliability and maintenance studies, hazard-rate function plays an important role, since it describes the instantaneous failure probability given that the individual survived or the item has not failed up to that instance. Sudden changes in the hazard function may occur due to overhauls, treatment effects or maintenance activities. A problem of statistical interest is th...