This tutorial gives a basic yet rigorous introduction to observable operator models (OOMs). OOMs are a recently discovered class of models of stochastic processes. They are mathematically simple in that they require only concepts from elementary linear algebra. The linear algebra nature gives rise to an efficient, consistent, unbiased, constructive learning procedure for estimating models from empirical data. The tutorial describes in detail the mathematical foundations and the practical use of OOMs for identifying and predicting discrete-time, discrete-valued processes, both for output-only and input-output systems
This report describes how the finite-dimensional marginal distributions of a stochastic process with...
The focus of this chapter is on models with discrete states. The system of states evolves according ...
We study normal approximations for a class of discrete-time occupancy processes, namely, Markov chai...
1This tutorial is updated at irregular intervals. For version control, use date underneath title. Th...
Observable operator models (OOMs) are a generalization of hidden Markov models (HMMs). They support ...
The article describes a new formal approach to model discrete stochastic processes, called observabl...
Observable operator models (OOMs), a recently developed matrix model class of stochastic processes [...
Observable operator models (OOMs) are matrix models for describing stochastic processes. In this rep...
Hidden Markov Models (HMMs) today are the method of choice for blackbox modelling of symbolic, stoch...
Abstract: This article introduces observable operator models (OOM) and conditioned continuation repr...
Observable operator models (OOMs) generalize hidden Markov models (HMMs) and can be represented in a...
By the means of the method of stochastization of one-step processes we get the simplified mathematic...
Partially Observable Markov Decision Processes (POMDPs) provide a rich representation for agents act...
Acceped to the 20th International Conference on Foundations of Software Science and Computation Stru...
This work focuses on Exchangeable Occupancy Models (EOM) and their relations with the Uniform Order ...
This report describes how the finite-dimensional marginal distributions of a stochastic process with...
The focus of this chapter is on models with discrete states. The system of states evolves according ...
We study normal approximations for a class of discrete-time occupancy processes, namely, Markov chai...
1This tutorial is updated at irregular intervals. For version control, use date underneath title. Th...
Observable operator models (OOMs) are a generalization of hidden Markov models (HMMs). They support ...
The article describes a new formal approach to model discrete stochastic processes, called observabl...
Observable operator models (OOMs), a recently developed matrix model class of stochastic processes [...
Observable operator models (OOMs) are matrix models for describing stochastic processes. In this rep...
Hidden Markov Models (HMMs) today are the method of choice for blackbox modelling of symbolic, stoch...
Abstract: This article introduces observable operator models (OOM) and conditioned continuation repr...
Observable operator models (OOMs) generalize hidden Markov models (HMMs) and can be represented in a...
By the means of the method of stochastization of one-step processes we get the simplified mathematic...
Partially Observable Markov Decision Processes (POMDPs) provide a rich representation for agents act...
Acceped to the 20th International Conference on Foundations of Software Science and Computation Stru...
This work focuses on Exchangeable Occupancy Models (EOM) and their relations with the Uniform Order ...
This report describes how the finite-dimensional marginal distributions of a stochastic process with...
The focus of this chapter is on models with discrete states. The system of states evolves according ...
We study normal approximations for a class of discrete-time occupancy processes, namely, Markov chai...