Research in Stochastic Processes and their Applications

Abstract

Research was conducted and directed in the following areas of stochastic processes and their applications in engineering, neurophysiology, and oceanography: Stochastic differential equations in infinite dimensional spaces; Stochastic differential equation models for spatially distributed neurons; Propagation of chaos for interacting systems; Nonlinear white noise analysis; Sampling designs for time series; Wavelets, Multiresolution decomposition, and Random processes; Non-Gaussian stable models (Structure and inference); Inference for linear and harmonizable time series; Periodically correlated and other nonstationary processes; Sample function properties; Random fields and their prediction; Markov random field models for vision; Point processes, Random sets, and Random measures; Random measures associated with high levels; and Tail inference for stationary sequences.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1993
Accession Number
ADA260174

Entities

People

  • Gopinath Kallianpur
  • M. Ross Leadbetter
  • Stamatis Cambanis

Organizations

  • University of North Carolina at Chapel Hill

Tags

Communities of Interest

  • Air Platforms
  • C4I
  • Human Systems

DTIC Thesaurus Topics

  • Computational Fluid Dynamics
  • Computational Science
  • Data Science
  • Differential Equations
  • Engineering
  • Gaussian Processes
  • Information Science
  • Markov Chains
  • Mathematical Filters
  • Mathematics
  • Partial Differential Equations
  • Probability
  • Random Variables
  • Stationary Processes
  • Statistical Algorithms
  • Statistics
  • Stochastic Processes

Readers

  • Acoustical Oceanography.
  • Statistical inference.
  • Wave Propagation and Nonlinear Chaotic Dynamics.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Space