Bayesian sequential parameter estimation

  • Mai T.
  • Wilson S.

A new approach of sequential parameter learning is proposed for a special general class of state-space model where the state equation is linear with Gaussian noise. In this approach, the filtering density of the state variable and unknown parameters is jointly and iteratively approximated by either Gaussian density or mixture Gaussian density. Compared to particle-based solutions, it is believed to be more robust to outlier and the traditional degeneracy problem due to the smooth functional approximation. Furthermore, this approach does not rely on the availability of sufficient statistics as in some recent particle-based parameter learning methods, and hence can be applied into different modelling classes. The approach is implemented in different variants, based on iterative Laplace approximation (iterLap, Bornkamp, 2011a), importance sampling (IS) and expectation-maximization (EM), and then analysed in various examples.

Recent Publications

August 09, 2017

A Cloud Native Approach to 5G Network Slicing

  • Francini A.
  • Miller R.
  • Sharma S.

5G networks will have to support a set of very diverse and often extreme requirements. Network slicing offers an effective way to unlock the full potential of 5G networks and meet those requirements on a shared network infrastructure. This paper presents a cloud native approach to network slicing. The cloud ...

August 01, 2017

Modeling and simulation of RSOA with a dual-electrode configuration

  • De Valicourt G.
  • Liu Z.
  • Violas M.
  • Wang H.
  • Wu Q.

Based on the physical model of a bulk reflective semiconductor optical amplifier (RSOA) used as a modulator in radio over fiber (RoF) links, the distributions of carrier density, signal photon density, and amplified spontaneous emission photon density are demonstrated. One of limits in the use of RSOA is the lower ...

July 12, 2017

PrivApprox: Privacy-Preserving Stream Analytics

  • Chen R.
  • Christof Fetzer
  • Le D.
  • Martin Beck
  • Pramod Bhatotia
  • Thorsten Strufe

How to preserve users' privacy while supporting high-utility analytics for low-latency stream processing? To answer this question: we describe the design, implementation and evaluation of PRIVAPPROX, a data analytics system for privacy-preserving stream processing. PRIVAPPROX provides three properties: (i) Privacy: zero-knowledge privacy (ezk) guarantees for users, a privacy bound tighter ...