Bayesian inference is one of two dominant approaches to statistical inference. The word Bayesian refers to the influence of Reverend Thomas Bayes. Bayesian inference is a modern revival of the classical definition of probability.
development of self-healing systems capable of making inferences about their own behavior, such as diagnosing faults and performance degradations. uses a cost-efficient technique for adaptive diagnosis that combines probabilistic inference with online, active selection of the most-informative measurements called probes. Probes are end-to-end test transactions that collect information about the availability and performance of a distributed system. Given the probe results (symptoms), RAIL performs Bayesian inference in order to find the most likely explanation (cause), An important difference between RAIL's approach and ''passive'' data analysis is in RAIL's ability to select and execute probes online. This approach, called active probing, uses an information-theoretic criterion called information gain in order to select adaptively only a small set of the most informative probes at any given time; this approach significantly reduces the overall number of probes required
4sr is an extension of 4store where we are implementing backward chained reasoning. Currently a subset of RDFS is supported. This set includes: rdfs:subClassOf, rdfs:subPropertyOf, rdfs:domain and rdfs:range.
Multiple channels and types of events…
… executing in multiple Inference Agents (Event Processing Agents on an Event Processing Network)…
… where Events drive Production Rules with associated (shared) data…
… and event patterns (complex events) are derived from the simple events and also drive Production Rules via inferencing…
… to lead to “real-time” decisions.
P. Chapman, G. Stapleton, J. Howse, and I. Oliver. 2011 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), page 87-94. (September 2011)
A. Morabia. American journal of epidemiology, 178 (10):
1526-32(November 2013)JID: 7910653; OTO: NOTNLM; aheadofprint;<m:linebreak></m:linebreak>Causalitat.
V. Alexiev. Workshop on Semantic Digital Archives (SDA 2012), part of International Conference on Theory and Practice of Digital Libraries (TPDL 2012), 912, Paphos, Cyprus, CEUR WS, (September 2012)
V. Alexiev, D. Manov, J. Parvanova, and S. Petrov. Workshop Practical Experiences with CIDOC CRM and its Extensions (CRMEX 2013) at TPDL 2013, 1117, Valetta, Malta, CEUR WS, (September 2013)
G. Marinov, V. Alexiev, and Y. Djonev. Artifical Intelligence: Methodology, Systems, and Applications (AIMSA'94), page 109-118. Sofia, Bulgaria, World Scientific Publishing, (September 1994)
X. Zheng, C. Dan, B. Aragam, P. Ravikumar, and E. Xing. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, volume 108 of Proceedings of Machine Learning Research, page 3414--3425. PMLR, (26--28 Aug 2020)
J. Huggins, M. Kasprzak, T. Campbell, and T. Broderick. (2019)cite arxiv:1910.04102Comment: A python package for carrying out our validated variational inference workflow -- including doing black-box variational inference and computing the bounds we develop in this paper -- is available at https://github.com/jhuggins/viabel. The same repository also contains code for reproducing all of our experiments.