Online variational inference for state-space models with point-process observations
We present a variational Bayesian (VB) approach for the state and parameter inference of a state-space model with point-process observations, a physiologically plausible model for signal processing of spike data. We also give the derivation of a variational smoother, as well as an efficient online f...
Main Authors: | Zammit Mangion, Anderw (Author), Yuan, Ke (Author), Kadirkamanathan, Visakan (Author), Niranjan, Mahesan (Author), Sanguinetti, Guido (Author) |
---|---|
Format: | Article |
Language: | English |
Published: |
2011-08.
|
Subjects: | |
Online Access: | Get fulltext |
Similar Items
-
Estimating a State-Space Model from Point Process Observations: A Note on Convergence
by: Yuan, Ke, et al.
Published: (2010) -
Markov chain Monte Carlo methods for state-space models with point process observations
by: Yuan, Ke, et al.
Published: (2012) -
Sequential learning in artifical neural networks
by: Kadirkamanathan, Visakan
Published: (1991) -
Reducing the algorithmic variability in transcriptome-based inference
by: Tuna, Salih, et al.
Published: (2010) -
Inference from Low Precision Transcriptome Data Representation
by: Tuna, Salih, et al.
Published: (2010)