MAP state sequence estimation for jump Markov linear systems via the expectation-maximization algorithm

2002 ◽  
Author(s):  
A. Logothetis ◽  
V. Krishnamurthy
Keyword(s):  
Linear Systems ◽  
Expectation Maximization ◽  
Expectation Maximization Algorithm ◽  
State Sequence ◽  
Sequence Estimation

scholarly journals Dynamic Expectation Maximization Algorithm for Estimation of Linear Systems with Colored Noise

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1306
Author(s):  
Ajith Anil Meera ◽  
Martijn Wisse
Keyword(s):  
Linear Systems ◽  
Expectation Maximization ◽  
Colored Noise ◽  
Learning Algorithm ◽  
Input Parameter ◽  
Mathematical Formulation ◽  
Expectation Maximization Algorithm ◽  
Superior Performance ◽  
Linear Estimator ◽  
Energy Principle

The free energy principle from neuroscience has recently gained traction as one of the most prominent brain theories that can emulate the brain’s perception and action in a bio-inspired manner. This renders the theory with the potential to hold the key for general artificial intelligence. Leveraging this potential, this paper aims to bridge the gap between neuroscience and robotics by reformulating an FEP-based inference scheme—Dynamic Expectation Maximization—into an algorithm that can perform simultaneous state, input, parameter, and noise hyperparameter estimation of any stable linear state space system subjected to colored noises. The resulting estimator was proved to be of the form of an augmented coupled linear estimator. Using this mathematical formulation, we proved that the estimation steps have theoretical guarantees of convergence. The algorithm was rigorously tested in simulation on a wide variety of linear systems with colored noises. The paper concludes by demonstrating the superior performance of DEM for parameter estimation under colored noise in simulation, when compared to the state-of-the-art estimators like Sub Space method, Prediction Error Minimization (PEM), and Expectation Maximization (EM) algorithm. These results contribute to the applicability of DEM as a robust learning algorithm for safe robotic applications.

Ordered subset expectation maximization algorithm for image reconstruction applied to zero-flow or low-flow mimicking brain phantom

2005 ◽  
Vol 25 (1_suppl) ◽  
pp. S678-S678
Author(s):  
Yasuhiro Akazawa ◽  
Yasuhiro Katsura ◽  
Ryohei Matsuura ◽  
Piao Rishu ◽  
Ansar M D Ashik ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Expectation Maximization ◽  
Expectation Maximization Algorithm ◽  
Low Flow ◽  
Brain Phantom ◽  
Zero Flow
Sign in / Sign up

Export Citation Format

Share Document