Distributed Dynamic Estimation in Diffusion Networks

Papers resulting from the project (and related)


Most of them can be downloaded from HERE.


  • [1] K. Dedecius and V. Sečkárová (2013). Dynamic Diffusion Estimation in Exponential Family Models. IEEE Signal Process. Lett., vol. 20, no. 11, pp. 1114–1117. (published before project start, relevant).
  • [2] K. Dedecius and V. Sečkárová (2014). Distributed Modelling of Big Dynamic Data with Generalized Linear Models. In Proceedings of the 17th International Conference on Information Fusion, Spain.
  • [3] K. Dedecius, J. Reichl and P.M. Djurić: Dynamic Mixture Estimation in Diffusion Networks. IEEE Signal Process. Lett., vol. 22, no. 2, pp. 197–201, 2015.
  • [4] K. Dedecius: Diffusion Estimation Of State-Space Models: Bayesian Formulation. In Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2014), IEEE, 2014.)
  • [5] K. Dedecius: Collaborative Kalman Filtration: Bayesian Perspective. In Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2014), pp. 468-474, INSTICC, 2014.
    • --- Example: Collaborative Kalman Filter [ipython notebook / rendered]
    • Remark: The networkx module has changed, hence the network fig. misses numbers.

  • [6] K. Dedecius: Information Fusion with Functional Bregman Divergence. UTIA Research Report 2345, 2015.

  • [7] K. Dedecius and J. Reichl: Distributed Estimation of Mixture Models. In Bayesian Statistics from Methods to Models and Applications. Springer Proceedings in Mathematics and Statistics, pp. 27-36, 2015.

  • [8] K. Dedecius: Adaptive Approximate Filtering of State-Space Models. In Proceedings of 23rd European Signal Processing Conference (EUSIPCO 2015), pp. 2236-2240, 2015.
  • [9] K. Dedecius and P.M. Djurić: Diffusion Filtration with Approximate Bayesian Computation. In Proceedings of 2015 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2015), pp. 3207-3211, 2015.

  • [10] J. Reichl and K. Dedecius: Diffusion MCMC for Mixture Estimation. UTIA Research Report 2354, 2016.

  • [11] J. Reichl and K. Dedecius: Likelihood Tempering in Dynamic Model Averaging. 3rd Young Bayesian Statisticians Meeting (BAYSM), 2016.

  • [12] K. Dedecius and V. Sečkárová: Diffusion Estimation of Mixture Models with Local and Global Parameters. In Proc. 2016 Statistical Signal Processing Workshop (SSP2016), pp. 362-365, 2016.

  • [13] K. Dedecius and P.M. Djurić: Sequential Estimation and Diffusion of Information Over Networks: A Bayesian Approach with Exponential Family of Distributions. IEEE Trans. Signal Process., vol. 65, no. 7, pp. 1795-1809, 2017. [Source codes]

  • [14] K. Dedecius: Adaptive Kernels in Approximate Filtering of State-Space Models. Int. J. Adapt Control Signal Process., vol. 31, no. 6, pp. 938-952, 2017.

  • [15] K. Dedecius and V. Sečkárová: Factorized Estimation of Partially Shared Parameters in Diffusion Networks. IEEE Trans. Signal Process., vol. 65, no 19, pp. 5153-5163, 2017

  • [16] K. Dedecius: Marginalized Approximate Filtering of State-Space Models. Int. J. Adapt. Control, doi:10.1002/acs.2821