On the Trackability of Stochastic Processes Based on Causal Information

Bacinoglu B. T., Sun Y., UYSAL E.

2020 IEEE International Symposium on Information Theory, ISIT 2020, California, United States Of America, 21 - 26 July 2020, pp.2228-2233 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/isit44484.2020.9174285
  • City: California
  • Country: United States Of America
  • Page Numbers: pp.2228-2233
  • Keywords: tracking conditions, causal information, Holder inequality, Renyi entropy, Gallager's reliability function, Gartner-Ellis limit, anytime capacity, causal estimation, ANYTIME CAPACITY, CHANNELS
  • Middle East Technical University Affiliated: Yes


© 2020 IEEE.We consider the problem of tracking an unstable stochastic process Xt by using causal knowledge of another stochastic process Yt. We obtain necessary conditions and sufficient conditions for maintaining a finite tracking error. We provide necessary conditions as well as sufficient conditions for the success of this estimation, which is defined as order m moment trackability. By-products of this study are connections between statistics such as Rényi entropy, Gallager's reliability function, and the concept of anytime capacity.