Bayesian Nonparametrics in Computer Science: Scalable Inference for Dynamic, Unbounded, and Streaming Data
Abstract
Bayesian nonparametrics is a powerful model that includes the complex, dynamic and unlimited data, which is why it is highly applicable in the current computer science practice. The Bayesian nonparametrics, in contrast to the traditional, parametric models, enable flexible, scalable inference, using the infinite-dimensional priors, which are the ability to process streaming data and continuous updates. This paper discusses the use of Bayesian nonparametrics in computer science, as the technology may support scalable inference in settings where the data continually grows and becomes unlimited. The principal goals are to learn the theoretical background, investigate the practical implementation and the efficiency of those models on the real life. Among the main results, it is possible to note that Bayesian nonparametric tests considerably enhance the functioning of real-time systems, which allows processing large and dynamic data efficiently. The implications also indicate how they can be applied in machine learning, AI, and data mining where continuous and scalable inference has a huge role to play in adapting to the evolving data streams.
Keywords
Bayesian Inference, Nonparametric Models, Scalable Inference, Streaming Data, Real-time Systems, Prediction Accuracy
Citations
IRE Journals:
Syed Khundmir Azmi
"Bayesian Nonparametrics in Computer Science: Scalable Inference for Dynamic, Unbounded, and Streaming Data" Iconic Research And Engineering Journals Volume 5 Issue 10 2022 Page 399-407
IEEE:
Syed Khundmir Azmi
"Bayesian Nonparametrics in Computer Science: Scalable Inference for Dynamic, Unbounded, and Streaming Data" Iconic Research And Engineering Journals, 5(10)
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