A Monte Carlo EM Approach for Partially Observable Diffusion Processes: Theory and Applications to Neural Networks | Citedby Results | Neural Computation | MIT Press

Skip to Main Content

Skip Nav Destination

A Monte Carlo EM Approach for Partially Observable Diffusion Processes: Theory and Applications to Neural Networks

Neural Computation (2002) 14 (7): 1507–1544.

This article has been cited by the following articles in journals that are participating in Crossref Cited-by Linking.

A review on generative Boltzmann networks applied to dynamic systems

Rémi Souriau
Jean Lerbet
Hsin Chen
Vincent Vigneron

Mechanical Systems and Signal Processing (2021) 147: 107072.

Nonlinear Bayesian filtering and learning: a neuronal dynamics for perception

Anna Kutschireiter
Simone Carlo Surace
Henning Sprekeler
Jean-Pascal Pfister

Scientific Reports (2017) 7 (1)

The Diffusion Network in Analog VLSI Exploiting Noise-Induced Stochastic Dynamics to Regenerate Various Continuous Paths

Yi-Da Wu
Hsin Chen

IEEE Transactions on Circuits and Systems I: Regular Papers (2015) 62 (6): 1617.

Prediction of Depth of Sedation from Biological Signals Using Continuous Restricted Boltzmann Machine

Yeou-Jiunn Chen
Shih-Chung Chen
Pei-Jarn Chen

Mathematical Problems in Engineering (2014) 2014: 1.

Algorithmic estimation of risk factors in financial markets with stochastic drift

Janko Hernández
David Saunders
Luis Seco

Computers & Operations Research (2012) 39 (4): 820.

Probabilistic neural computing with advanced nanoscale MOSFETs

Nor Hisham Hamid
Tong Boon Tang
Alan F. Murray

Neurocomputing (2011) 74 (6): 930.

Parametric estimation for partially hidden diffusion processes sampled at discrete times

Stefano Maria Iacus
Masayuki Uchida
Nakahiro Yoshida

Stochastic Processes and their Applications (2009) 119 (5): 1580.

Continuous-valued probabilistic behavior in a VLSI generative model

P.C.D. Fleury
A.F. Murray

IEEE Transactions on Neural Networks (2006) 17 (3): 755.

Parametric Estimation for Partially Hidden Diffusion Processes Sampled at Discrete Times

Stefano Maria Iacus
Masayuki Uchida
Nakahiro Yoshida

SSRN Electronic Journal (2006)

Continuous restricted Boltzmann machine with an implementable training algorithm

H. Chen
A.F. Murray

IEE Proceedings - Vision, Image, and Signal Processing (2003) 150 (3): 153.