Memoryless representation of Markov processes

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Memoryless processes hold many theoretical and practical advantages. They are easy to describe, analyze, store and encrypt. They can also be seen as the essence of a family of regression processes, or as an innovation process triggering a dynamic system. The Gram-Schmidt procedure suggests a linear sequential method of whitening (decorrelating) any stochastic process. Applied on a Gaussian process, memorylessness (that is, statistical independence) is guaranteed. It is not clear however, how to sequentially construct a memoryless process from a non-Gaussian process. In this paper we present a non-linear sequential method to generate a memoryless process from any given Markov process under varying objectives and constraints. We differentiate between lossless and lossy methods, closed form and algorithmic solutions and discuss the properties and uniqueness of our suggested methods.

Original languageEnglish
Title of host publication2013 IEEE International Symposium on Information Theory, ISIT 2013
Number of pages5
StatePublished - 2013
Event2013 IEEE International Symposium on Information Theory, ISIT 2013 - Istanbul, Turkey
Duration: 7 Jul 201312 Jul 2013

Publication series

NameIEEE International Symposium on Information Theory - Proceedings


Conference2013 IEEE International Symposium on Information Theory, ISIT 2013


  • Gram-Schmidt procedure
  • Markov procceses
  • memoryless processes
  • optimal transportation problem

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Information Systems
  • Modelling and Simulation
  • Applied Mathematics


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