HIGH-ORDER CONDITIONAL DISTANCE COVARIANCE WITH CONDITIONAL MUTUAL INDEPENDENCE

2020 ◽  
pp. 1-18
Author(s):  
Pengfei Liu ◽  
Xuejun Ma ◽  
Wang Zhou
Keyword(s):  
Linear Combination ◽  
Conditional Independence ◽  
Graphical Model ◽  
High Order ◽  
Gaussian Graphical Model ◽  
Random Vectors ◽  
Independence Test ◽  
Mutual Independence ◽  
Simulation Results ◽  
Distance Covariance

We construct a high-order conditional distance covariance, which generalizes the notation of conditional distance covariance. The joint conditional distance covariance is defined as a linear combination of conditional distance covariances, which can capture the joint relation of many random vectors given one vector. Furthermore, we develop a new method of conditional independence test based on the joint conditional distance covariance. Simulation results indicate that the proposed method is very effective. We also apply our method to analyze the relationships of PM2.5 in five Chinese cities: Beijing, Tianjin, Jinan, Tangshan and Qinhuangdao by the Gaussian graphical model.

The Costs of Ignoring High-Order Correlations in Populations of Model Neurons

2006 ◽  
Vol 18 (3) ◽  
pp. 660-682 ◽  
Author(s):  
Melchi M. Michel ◽  
Robert A. Jacobs
Keyword(s):  
Model Neuron ◽  
Joint Probability ◽  
High Order ◽  
Neural Population ◽  
Neural Responses ◽  
Significant Information ◽  
Statistical Correlations ◽  
Neural Populations ◽  
Simulation Results ◽  
Dependence Tree

Investigators debate the extent to which neural populations use pairwise and higher-order statistical dependencies among neural responses to represent information about a visual stimulus. To study this issue, three statistical decoders were used to extract the information in the responses of model neurons about the binocular disparities present in simulated pairs of left-eye and right-eye images: (1) the full joint probability decoder considered all possible statistical relations among neural responses as potentially important; (2) the dependence tree decoder also considered all possible relations as potentially important, but it approximated high-order statistical correlations using a computationally tractable procedure; and (3) the independent response decoder, which assumed that neural responses are statistically independent, meaning that all correlations should be zero and thus can be ignored. Simulation results indicate that high-order correlations among model neuron responses contain significant information about binocular disparities and that the amount of this high-order information increases rapidly as a function of neural population size. Furthermore, the results highlight the potential importance of the dependence tree decoder to neuroscientists as a powerful but still practical way of approximating high-order correlations among neural responses.

An Improved Acquisition Method for High-Order BOC-Modulated Signals Based on Fractal Geometry

2013 ◽  
Vol 846-847 ◽  
pp. 1185-1188 ◽  
Author(s):  
Hua Bing Wu ◽  
Jun Liang Liu ◽  
Yuan Zhang ◽  
Yong Hui Hu
Keyword(s):  
Fractal Geometry ◽  
Output Signal ◽  
Signal To Noise Ratio ◽  
High Order ◽  
Main Peak ◽  
Signal To Noise ◽  
Binary Offset Carrier ◽  
Simulation Results ◽  
Modulated Signals ◽  
Acquisition Method

This paper proposes an improved acquisition method for high-order binary-offset-carrier (BOC) modulated signals based on fractal geometry. We introduced the principle of our acquisition method, and outlined its framework. We increase the main peak to side peaks ratio in the BOC autocorrelation function (ACF), with a simple fractal geometry transform. The proposed scheme is applicable to both generic high-order sine-and cosine-phased BOC-modulated signals. Simulation results show that the proposed method increases output signal to noise ratio (SNR).

Operational Transconductance Amplifier-Based Electronically Controllable Memcapacitor and Meminductor Emulators

2021 ◽  
pp. 2150222
Author(s):  
Eyyup demir ◽  
Abdullah Yesil ◽  
Yunus Babacan ◽  
Tevhit Karacali
Keyword(s):  
Process Parameters ◽  
High Order ◽  
Crucial Importance ◽  
Operational Transconductance Amplifier ◽  
Passive Components ◽  
Mos Transistor ◽  
Transconductance Amplifier ◽  
Simulation Results ◽  
Layout Area ◽  
Electronically Controllable

In this paper, two simple circuits are presented to emulate both memcapacitor and meminductor circuit elements. The emulation of these components has crucial importance since obtaining these high-order elements from markets is difficult when compared to resistor, capacitor and inductor. For this reason, we proposed Multi-Output Operational Transconductance Amplifier (MO-OTA)-based electronically controllable memcapacitor and meminductor circuits. To operate the MOS transistor as a capacitor, drain and source terminals are connected to each other. The memcapacitor behavior is obtained by driving the connected terminals with suitable voltage values. Only a few active and grounded passive components which are found in markets easily are used to emulate meminductive behavior. Furthermore, all passive elements in the circuit are grounded. All simulation results for memcapacitor and meminductor emulators are obtained successfully when compared to previous studies. For all analyses, MO-OTA is laid using the Cadence Spectre Analog Environment with TSMC 0.18[Formula: see text][Formula: see text]m process parameters and occupied a layout area of only 86.21[Formula: see text][Formula: see text]m.

Tensor-Based Gaussian Graphical Model

2021 ◽  
pp. 285-298
Author(s):  
Yipeng Liu ◽  
Jiani Liu ◽  
Zhen Long ◽  
Ce Zhu
Keyword(s):  
Graphical Model ◽  
Gaussian Graphical Model

scholarly journals Testing the Structure of a Gaussian Graphical Model With Reduced Transmissions in a Distributed Setting

2019 ◽  
Vol 67 (20) ◽  
pp. 5391-5401 ◽  
Author(s):  
Yicheng Chen ◽  
Rick S. Blum ◽  
Brian M. Sadler ◽  
Jiangfan Zhang
Keyword(s):  
Graphical Model ◽  
Gaussian Graphical Model
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