Hidden Process Models

Abstract

We introduce the Hidden Process Model (HPM),a probabilistic model for multivariate time series data intended to model complex, poorly understood, overlapping and linearly additive processes. HPMs are motivated by our interest in modeling cognitive processes given brain image data. We define HPMs. present inference and learning algorithms study their characteristics using synthetic data, and demonstrate their use for tracking human cognitive processes using fMRI data.

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Document Details

Document Type
Technical Report
Publication Date
Feb 17, 2006
Accession Number
ADA455958

Entities

People

  • Indrayana Rustandi
  • Rebecca Hutchinson
  • Tom M. Mitchell

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Bayesian Networks
  • Cognition
  • Computer Science
  • Data Analysis
  • Generative Models
  • Hidden Markov Models
  • Machine Learning
  • Magnetic Resonance
  • Markov Models
  • Models
  • Neuroimaging
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Random Variables

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Neuroscience
  • Quantum Dot Semiconductor Device Photonics and Graphene Optoelectronic Materials and THz Physics.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks