AIMC Topic: Markov Chains

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Hidden Markov modeling of frequency-following responses to Mandarin lexical tones.

Journal of neuroscience methods
BACKGROUND: The frequency-following response (FFR) is a scalp-recorded electrophysiological potential reflecting phase-locked activity from neural ensembles in the auditory system. The FFR is often used to assess the robustness of subcortical pitch p...

Developmental Approach for Behavior Learning Using Primitive Motion Skills.

International journal of neural systems
Imitation learning through self-exploration is essential in developing sensorimotor skills. Most developmental theories emphasize that social interactions, especially understanding of observed actions, could be first achieved through imitation, yet t...

DropConnected neural networks trained on time-frequency and inter-beat features for classifying heart sounds.

Physiological measurement
OBJECTIVE: Automatic heart sound analysis has the potential to improve the diagnosis of valvular heart diseases in the primary care phase, as well as in countries where there is neither the expertise nor the equipment to perform echocardiograms. An a...

Employing decomposable partially observable Markov decision processes to control gene regulatory networks.

Artificial intelligence in medicine
OBJECTIVE: Formulate the induction and control of gene regulatory networks (GRNs) from gene expression data using Partially Observable Markov Decision Processes (POMDPs).

Defining and characterizing the critical transition state prior to the type 2 diabetes disease.

PloS one
BACKGROUND: Type 2 diabetes mellitus (T2DM), with increased risk of serious long-term complications, currently represents 8.3% of the adult population. We hypothesized that a critical transition state prior to the new onset T2DM can be revealed throu...

A statistical framework for biomedical literature mining.

Statistics in medicine
In systems biology, it is of great interest to identify new genes that were not previously reported to be associated with biological pathways related to various functions and diseases. Identification of these new pathway-modulating genes does not onl...

Kernel dynamic policy programming: Applicable reinforcement learning to robot systems with high dimensional states.

Neural networks : the official journal of the International Neural Network Society
We propose a new value function approach for model-free reinforcement learning in Markov decision processes involving high dimensional states that addresses the issues of brittleness and intractable computational complexity, therefore rendering the v...

Passivity analysis of neural networks with two different Markovian jumping parameters and mixed time delays.

ISA transactions
This paper studies the problem of passivity analysis for neural networks with two different Markovian jumping parameters and mixed time delays utilizing some integral inequalities. The integral inequalities produce sharper bounds than what the Jensen...

Non-fragile mixed H∞ and passive synchronization of Markov jump neural networks with mixed time-varying delays and randomly occurring controller gain fluctuation.

PloS one
This paper studies the non-fragile mixed H∞ and passive synchronization problem for Markov jump neural networks. The randomly occurring controller gain fluctuation phenomenon is investigated for non-fragile strategy. Moreover, the mixed time-varying ...

Hidden Markov model and Chapman Kolmogrov for protein structures prediction from images.

Computational biology and chemistry
Protein structure prediction and analysis are more significant for living organs to perfect asses the living organ functionalities. Several protein structure prediction methods use neural network (NN). However, the Hidden Markov model is more interpr...