Bayesian Density Estimation and Inference Using Mixtures

1995 ◽  
Vol 90 (430) ◽  
pp. 577-588 ◽  
Author(s):  
Michael D. Escobar ◽  
Mike West
Keyword(s):  
Density Estimation ◽  
Bayesian Density Estimation

scholarly journals Bayesian density estimation and model selection using nonparametric hierarchical mixtures

2010 ◽  
Vol 54 (4) ◽  
pp. 816-832 ◽  
Author(s):  
Raffaele Argiento ◽  
Alessandra Guglielmi ◽  
Antonio Pievatolo
Keyword(s):  
Model Selection ◽  
Density Estimation ◽  
Bayesian Density Estimation

scholarly journals Adaptive Bayesian density estimation with location-scale mixtures

2010 ◽  
Vol 4 (0) ◽  
pp. 1225-1257 ◽  
Author(s):  
Willem Kruijer ◽  
Judith Rousseau ◽  
Aad van der Vaart
Keyword(s):  
Density Estimation ◽  
Scale Mixtures ◽  
Bayesian Density Estimation

scholarly journals Bayesian Pixel Density Estimation Modeling to Detect Human Sperm Sample Image Based on Sperm Head Shape

SinkrOn ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 91-99
Author(s):  
Candra Zonyfar ◽  
Kiki Ahmad Baihaqi
Keyword(s):  
Density Estimation ◽  
Human Sperm ◽  
Sperm Head ◽  
Zernike Moment ◽  
Estimation Model ◽  
Computational Accuracy ◽  
Proposed Model ◽  
Normal Human ◽  
Bayesian Density Estimation ◽  
Hu Moment

Currently, there is a problem of the difficulty in classifying human sperm head sample images using different databases and measuring the accuracy of several different datasets. This study proposes a Bayesian Density Estimation-based model for detecting human sperm heads with four classification labels, namely, normal, tapered, pyriform, and small or amorphous. This model was applied to three kinds of datasets to detect the level of pixel density in images containing normal human sperm head samples. Experimental results and computational accuracy are also presented. As a method, this study labeled each human sperm head based on three shape descriptors using the formulas of Hu moment, Zernike moment, and Fourier descriptor. Each descriptor was also tested in the experiment. There was an increased accuracy that reached 90% after the model was applied to the three datasets. The Bayesian Density Estimation model could classify images containing human sperm head samples. The correct classification level was obtained when the human sperm head was detected by combining Bayesian + Hu moment with an accuracy rate of up to 90% which could detect normal human sperm heads. It is concluded that the proposed model can detect and classify images containing human sperm head objects. This model can increase accuracy, so it is very appropriate to be applied in the medical field

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