Markov Chain Monte Carlo Methods for State-Space Models with Point Process Observations

Riemann manifold Langevin and Hamiltonian Monte Carlo methods

(2011) Mark Girolami et al.   JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY

Online Variational Inference for State-Space Models with Point-Process Observations

(2011) Andrew Zammit Mangion et al.   NEURAL COMPUTATION

Neuronal Spike Train Analysis in Likelihood Space

(2011) Yousef Salimpour et al.   PLoS One

Approximate Methods for State-Space Models

(2010) Shinsuke Koyama et al.   JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION

Particle Markov chain Monte Carlo methods

(2010) Christophe Andrieu et al.   JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY

Estimating a State-Space Model from Point Process Observations: A Note on Convergence

(2010) Ke Yuan et al.   NEURAL COMPUTATION

Efficient Markov Chain Monte Carlo Methods for Decoding Neural Spike Trains

(2010) Yashar Ahmadian et al.   NEURAL COMPUTATION

Sequential Monte Carlo Point-Process Estimation of Kinematics from Neural Spiking Activity for Brain-Machine Interfaces

(2009) Yiwen Wang et al.   NEURAL COMPUTATION

A benchmark test for a quantitative assessment of simple neuron models

(2007) Renaud Jolivet et al.   JOURNAL OF NEUROSCIENCE METHODS