Probabilistic classificationIn machine learning, a probabilistic classifier is a classifier that is able to predict, given an observation of an input, a probability distribution over a set of classes, rather than only outputting the most likely class that the observation should belong to. Probabilistic classifiers provide classification that can be useful in its own right or when combining classifiers into ensembles. Formally, an "ordinary" classifier is some rule, or function, that assigns to a sample x a class label ŷ: The samples come from some set X (e.
Data analysisData analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today's business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively.
Hybrid kernelA hybrid kernel is an operating system kernel architecture that attempts to combine aspects and benefits of microkernel and monolithic kernel architectures used in operating systems. The traditional kernel categories are monolithic kernels and microkernels (with nanokernels and exokernels seen as more extreme versions of microkernels). The "hybrid" category is controversial, due to the similarity of hybrid kernels and ordinary monolithic kernels; the term has been dismissed by Linus Torvalds as simple marketing.
Bayes factorThe Bayes factor is a ratio of two competing statistical models represented by their evidence, and is used to quantify the support for one model over the other. The models in questions can have a common set of parameters, such as a null hypothesis and an alternative, but this is not necessary; for instance, it could also be a non-linear model compared to its linear approximation. The Bayes factor can be thought of as a Bayesian analog to the likelihood-ratio test, although it uses the (integrated) marginal likelihood rather than the maximized likelihood.
Mach (kernel)Mach (mɑːk) is a kernel developed at Carnegie Mellon University by Richard Rashid and Avie Tevanian to support operating system research, primarily distributed and parallel computing. Mach is often considered one of the earliest examples of a microkernel. However, not all versions of Mach are microkernels. Mach's derivatives are the basis of the operating system kernel in GNU Hurd and of Apple's XNU kernel used in macOS, iOS, iPadOS, tvOS, and watchOS. The project at Carnegie Mellon ran from 1985 to 1994, ending with Mach 3.
Kernel (category theory)In and its applications to other branches of mathematics, kernels are a generalization of the kernels of group homomorphisms, the kernels of module homomorphisms and certain other kernels from algebra. Intuitively, the kernel of the morphism f : X → Y is the "most general" morphism k : K → X that yields zero when composed with (followed by) f. Note that kernel pairs and difference kernels (also known as binary equalisers) sometimes go by the name "kernel"; while related, these aren't quite the same thing and are not discussed in this article.
OpenBSDOpenBSD is a security-focused, free and open-source, Unix-like operating system based on the Berkeley Software Distribution (BSD). Theo de Raadt created OpenBSD in 1995 by forking NetBSD 1.0. According to the website, the OpenBSD project emphasizes "portability, standardization, correctness, proactive security and integrated cryptography." The OpenBSD project maintains portable versions of many subsystems as packages for other operating systems.
