Capturing Heterogeneity in Preference for a Real Estate Offering Using a Hierarchical Bayesian Regression Model

2016 ◽  
Vol 38 (2) ◽  
pp. 291-320 ◽  
Author(s):  
Kanupriya Katyal ◽  
Jagrook Dawra
Author(s):  
Sabyasachi Mukhopadhyay ◽  
Joseph O. Ogutu ◽  
Gundula Bartzke ◽  
Holly T. Dublin ◽  
Hans-Peter Piepho

2011 ◽  
Vol 11 (3) ◽  
pp. 185-201 ◽  
Author(s):  
Gabriel Nuñez-Antonio ◽  
Eduardo Gutiérrez-Peña ◽  
Gabriel Escarela

2021 ◽  
Author(s):  
Daniel Habermann ◽  
Hadi Karimzadeh ◽  
Andreas Walker ◽  
Yang Li ◽  
Rongge Yang ◽  
...  

Motivation: A key process in anti-viral adaptive immunity is that the Human Leukocyte Antigen system (HLA) presents epitopes as Major Histocompatibility Complex I (MHC I) protein-peptide complexes on cell surfaces and in this way alerts CD8+ cytotoxic T-Lymphocytes (CTLs). This pathway exerts strong selection pressure on viruses, favoring viral mutants that escape recognition by the HLA/CTL system, e.g. by point mutations that decrease binding of viral peptides to MHC I. Naturally, such immune escape mutations often emerge in highly variable viruses, e.g. HIV or HBV, as HLA-associated mutations (HAMs), specific to the host HLA alleles and its MHC I proteins. The reliable identification of HAMs is not only important for understanding viral genomes and their evolution, but it also impacts the development of broadly effective anti-viral treatments and vaccines against variable viruses. By their very nature HAMs are amenable to detection by statistical methods in paired sequence / HLA data. However, HLA alleles are very polymorphic in the human host population which makes the available data relatively sparse and noisy. Under these circumstances, one way to optimize HAM detection is to integrate all relevant information in a coherent model. Bayesian inference offers a principled approach to achieve this. Results: We present a new regression model for the detection of HAMs. As we choose a Bayesian approach we can include the novel sparsity-inducing priors, and we obtain easily interpretable quantitative information on HAM candidates. The basic model can be extended to include prior information relevant to HAM detection, which we demonstrate by integrating predictions of epitope affinities to MHC I, predictions of epitope peptide processing, and computation of phylogenetic background. This integrative method improves performance in HAM detection considerably over state-of-the-art methods.


Author(s):  
Nannan Li ◽  
Xinyu Wu ◽  
Huiwen Guo ◽  
Dan Xu ◽  
Yongsheng Ou ◽  
...  

In this paper, we propose a new approach for anomaly detection in video surveillance. This approach is based on a nonparametric Bayesian regression model built upon Gaussian process priors. It establishes a set of basic vectors describing motion patterns from low-level features via online clustering, and then constructs a Gaussian process regression model to approximate the distribution of motion patterns in kernel space. We analyze different anomaly measure criterions derived from Gaussian process regression model and compare their performances. To reduce false detections caused by crowd occlusion, we utilize supplement information from previous frames to assist in anomaly detection for current frame. In addition, we address the problem of hyperparameter tuning and discuss the method of efficient calculation to reduce computation overhead. The approach is verified on published anomaly detection datasets and compared with other existing methods. The experiment results demonstrate that it can detect various anomalies efficiently and accurately.


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