MODELING PAVEMENT DETERIORATION PROCESSES BY POISSON HIDDEN MARKOV MODELS

A Hidden Markov Model for Modeling Pavement Deterioration under Incomplete Monitoring Data

In this paper, the potential use of an exponential hidden Markov model to model a hidden pavement deterioration process, i.e. one that is not directly measurable, is investigated. It is assumed that the evolution of the physical condition, which is the hidden process, and the evolution of the values of pavement distress indicators, can be adequately described using discrete condition states and...

Modeling manipulation interactions by hidden Markov models

This paper describes a new approach on how to teach everyday manipulation tasks to a robot under the “Learning from Observation” framework. In our previous work, to acquire low-level action primitives of a task automatically, we proposed a technique to estimate essential interactions to complete a task by integrating multiple observations of similar demonstrations. But after many demonstrations...

Hidden Markov Models with Multiple Observation Processes

We consider a hidden Markov model with multiple observation processes, one of which is chosen at each point in time by a policy—a deterministic function of the information state—and attempt to determine which policy minimises the limiting expected entropy of the information state. Focusing on a special case, we prove analytically that the information state always converges in distribution, and ...

Approximation of stationary processes by hidden Markov models

We propose an algorithm for the construction of a Hidden Markov Model (HMM) of assigned complexity (number of states of the underlying Markov chain) which best approximates, in Kullback-Leibler divergence rate, a given stationary process. We establish, under mild conditions, the existence of the divergence rate between a stationary process and an HMM, and approximate it with a properly defined ...

Multivariate Poisson Hidden Markov Models for Analysis of Spatial Counts