Distributed Dynamic Estimation in Diffusion Networks

- [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). - --- Presented: ICASSP 2014 [poster | ipython notebook / rendered]
- [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. - --- Example 1: Distributed linear regression [ipython notebook / rendered]
- --- Examples 2/3: Distributed logistic regression [ipython notebook / rendered]
- [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. - --- Example: Simulation of RLS [ipython notebook (diffusion) / rendered] + [ipython notebook (no coop.) / rendered] + [ipython notebook (analyses) / rendered]
- Remark: These files reproduce the results of paper accepted with minor revisions.
- [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.) - --- Poster presented at the workshop.
- --- Example: Diffusion Kalman filter [ipython notebook / rendered] + [ipython notebook (analyses) / rendered]
- Remark: This file reproduces the results of paper accepted before minor revisions.
- [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. - --- Example: SMC Filters [ipython notebook / rendered]
- [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