Inference for Continuous-Time Probabilistic Programming

Abstract

Machine learning the ability of computers to understand data, manage results and infer insights from uncertain information is the force behind many recent revolutions in computing. Email spam filters, smartphone personal assistants and self-driving vehicles are all based on research advances in machine learning. Unfortunately, even as the demand for these capabilities is accelerating, every new application requires a Herculean effort. Teams of hard-to-find experts must build expensive, custom tools that are often painfully slow and can perform unpredictably against large, complex data sets. This project developed new algorithms for statistical inference in continuous-time probabilistic models. This report first reviews background on continuous-time models and then covers each of the research projects and their deliverables. The full details are covered in the technical papers, included as appendices.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2017
Accession Number
AD1044912

Entities

People

  • Christian R. Shelton

Organizations

  • University of California, Riverside

Tags

Communities of Interest

  • Autonomy
  • C4I
  • Cyber
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Artificial Intelligence Software
  • Bayesian Networks
  • Computational Science
  • Computer Languages
  • Computer Programming
  • Data Mining
  • Dimensionality Reduction
  • Health Services
  • Information Processing
  • Information Science
  • Machine Learning
  • Network Science
  • Neural Networks
  • Operations Research
  • Probabilistic Models
  • Stochastic Processes

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Distributed Systems and Data Platform Development

Technology Areas

  • AI & ML