scholarly journals Belief Updating by Communication Channel

2007 ◽  
pp. 211-225 ◽  
Author(s):  
Shingo Hagiwara ◽  
Mikito Kobayashi ◽  
Satoshi Tojo
Keyword(s):  
Communication Channel ◽  
Belief Updating

Bayesian belief updating after a replication experiment

2018 ◽  
Vol 41 ◽  
Author(s):  
Alex O. Holcombe ◽  
Samuel J. Gershman
Keyword(s):  
Confirmation Theory ◽  
Bayesian Confirmation Theory ◽  
Belief Updating ◽  
Original Experiment ◽  
Bayesian Confirmation ◽  
Replication Experiment

AbstractZwaan et al. and others discuss the importance of the inevitable differences between a replication experiment and the corresponding original experiment. But these discussions are not informed by a principled, quantitative framework for taking differences into account. Bayesian confirmation theory provides such a framework. It will not entirely solve the problem, but it will lead to new insights.

Ultimate resolution and information in electron microscopy

1991 ◽  
Vol 49 ◽  
pp. 496-497
Author(s):  
D. Van Dyck
Keyword(s):  
Electron Microscopy ◽  
Electron Microscope ◽  
Transfer Function ◽  
Information Transfer ◽  
Communication Channel ◽  
Structural Information ◽  
Information Rate ◽  
Noise Power ◽  
Signal Power ◽  
Imaging Device

An (electron) microscope can be considered as a communication channel that transfers structural information between an object and an observer. In electron microscopy this information is carried by electrons. According to the theory of Shannon the maximal information rate (or capacity) of a communication channel is given by C = B log2 (1 + S/N) bits/sec., where B is the band width, and S and N the average signal power, respectively noise power at the output. We will now apply to study the information transfer in an electron microscope. For simplicity we will assume the object and the image to be onedimensional (the results can straightforwardly be generalized). An imaging device can be characterized by its transfer function, which describes the magnitude with which a spatial frequency g is transferred through the device, n is the noise. Usually, the resolution of the instrument ᑭ is defined from the cut-off 1/ᑭ beyond which no spadal information is transferred.

Strongly-handed Individuals are Less Gullible: Interhemispheric Interaction and Belief Updating

2006 ◽  
Author(s):  
Stephen D. Christman ◽  
Bradley R. Henning ◽  
Ruth E. Propper
Keyword(s):  
Interhemispheric Interaction ◽  
Belief Updating

Method for measuring distortions of a speech signal during its transmission over a communication channel to a biometric identification system

2020 ◽  
pp. 65-72
Author(s):  
V. V. Savchenko ◽  
A. V. Savchenko
Keyword(s):  
Speech Signal ◽  
Communication Channel ◽  
Speaker Identification ◽  
A Priori ◽  
Small Samples ◽  
Practical Implementation ◽  
Identification System ◽  
New Information ◽  
Discrimination Criterion ◽  
A Priori Uncertainty

This paper is devoted to the presence of distortions in a speech signal transmitted over a communication channel to a biometric system during voice-based remote identification. We propose to preliminary correct the frequency spectrum of the received signal based on the pre-distortion principle. Taking into account a priori uncertainty, a new information indicator of speech signal distortions and a method for measuring it in conditions of small samples of observations are proposed. An example of fast practical implementation of the method based on a parametric spectral analysis algorithm is considered. Experimental results of our approach are provided for three different versions of communication channel. It is shown that the usage of the proposed method makes it possible to transform the initially distorted speech signal into compliance on the registered voice template by using acceptable information discrimination criterion. It is demonstrated that our approach may be used in existing biometric systems and technologies of speaker identification.

METHOD OF ASSESSMENT OF THE COMMUNICATION CHANNEL BASED ON THE POLYNOMIAL MODEL OF PULSE CHARACTERISTICS

2019 ◽  
Vol 41 ◽  
pp. 53-62
Author(s):  
I. A. Batyrev ◽  
◽  
S. M. Dobrovolskiy ◽  
A. M. Semenov ◽  
Yu. F. Strugov ◽  
...  
Keyword(s):  
Communication Channel ◽  
Polynomial Model ◽  
Pulse Characteristics ◽  
Method Of Assessment
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