Hidden Process Model

AKA: HPM.
Example(s):
Counter-Example(s):
- Markov Process,
- Cognitive Process,
See: Dynamic Bayes Network, General Linear Model, Stochastic Process, Deterministic Process, Functional Data.

References

(Cgamroukhi et al., 2010) ⇒ Chamroukhi, F., Samé, A., Govaert, G., & Aknin, P. (2010). "A hidden process regression model for functional data description". application to curve discrimination. Neurocomputing, 73(7-9), 1210-1221.
- ABSTRACT: A new approach for functional data description is proposed in this paper. It consists of a regression model with a discrete hidden logistic process which is adapted for modeling curves with abrupt or smooth regime changes. The model parameters are estimated in a maximum likelihood framework through a dedicated Expectation Maximization (EM) algorithm. From the proposed generative model, a curve discrimination rule is derived using the Maximum A Posteriori rule. The proposed model is evaluated using simulated curves and real world curves acquired during railway switch operations, by performing comparisons with the piecewise regression approach in terms of curve modeling and classification.

(Hutchison, Mitchell & Rustandi, 2006) ⇒ Hutchinson, R. A., Mitchell, T. M., & Rustandi, I. (2006, June). "Hidden process models. In: Proceedings of the 23rd International Conference on Machine learning" (PDF) (pp. 433-440). ACM.
- ABSTRACT: We introduce Hidden Process Models (HPMs), a class of probabilistic models for multivariate time series data. The design of HPMs has been motivated by the challenges of modeling hidden cognitive processes in the brain, given functional Magnetic Resonance Imaging (fMRI) data. fMRI data is sparse, high-dimensional, non-Markovian, and often involves prior knowledge of the form "hidden event A occurs n times within the interval [t,t′]." HPMs provide a generalization of the widely used General Linear Model approaches to fMRI analysis, and HPMs can also be viewed as a subclass of Dynamic Bayes Networks.

(Newman et al., 2006) ⇒ Newman, K. B., Buckland, S. T., Lindley, S. T., Thomas, L., & Fernandez, C. (2006). "Hidden process models for animal population dynamics". Ecological Applications, 16(1), 74-86.
- ABSTRACT: Hidden process models are a conceptually useful and practical way to simultaneously account for process variation in animal population dynamics and measurement errors in observations and estimates made on the population. Process variation, which can be both demographic and environmental, is modeled by linking a series of stochastic and deterministic subprocesses that characterize processes such as birth, survival, maturation, and movement. Observations of the population can be modeled as functions of true abundance with realistic probability distributions to describe observation or estimation error. Computer‐intensive procedures, such as sequential Monte Carlo methods or Markov chain Monte Carlo, condition on the observed data to yield estimates of both the underlying true population abundances and the unknown population dynamics parameters. Formulation and fitting of a hidden process model are demonstrated for Sacramento River winter‐run chinook salmon (Oncorhynchus tshawytsha).