Bayesian Predictive Modeling Based on Multidimensional Connectivity Profiling
نویسندگان
چکیده
منابع مشابه
Scalable Multidimensional Hierarchical Bayesian Modeling on Spark
We consider the problem of estimating occurrence rates of rare events for extremely sparse data using pre-existing hierarchies and selected features to perform inference along multiple dimensions. In particular, we focus on the problem of estimating click rates for {Advertiser, Publisher, User} tuples where both the Advertisers and the Publishers are organized as hierarchies that capture broad ...
متن کاملBayesian Modeling Based on Data from the Internet of Things
The Internet of Things is suggested as the upcoming revolution in the Information and communication technology due to its very high capability of making various businesses and industries more productive and efficient. This productivity comes from the emergence of innovation and the introduction of new capabilities for businesses. Different industries have shown varying reactions to IOT, but wha...
متن کاملMolecular Profiling of Neurons Based on Connectivity
The complexity and cellular heterogeneity of neural circuitry presents a major challenge to understanding the role of discrete neural populations in controlling behavior. While neuroanatomical methods enable high-resolution mapping of neural circuitry, these approaches do not allow systematic molecular profiling of neurons based on their connectivity. Here, we report the development of an appro...
متن کاملA Moving Avarage Variation Control Chart based on Bayesian Predictive Density
Recently several control charts have been introduced in the statistical process control literature which are based on the idea of Bayesian Predictive Density (BPD). Among these charts is the variation control chart which we refer to it as VBPD chart. In this paper we add the idea of Moving Average to VBPD chart and introduce a new variation control chart which has all advantages of the ...
متن کاملBayesian Model Learning Based on Predictive Entropy
Bayesian paradigm has been widely acknowledged as a coherent approach to learning putative probability model structures from a finite class of candidate models. Bayesian learning is based on measuring the predictive ability of a model in terms of the corresponding marginal data distribution, which equals the expectation of the likelihood with respect to a prior distribution for model parameters...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: The Neuroradiology Journal
سال: 2015
ISSN: 1971-4009,2385-1996
DOI: 10.15274/nrj-2014-10111