The probabilistic relevance model was devised by Stephen E. Robertson and Karen Spärck Jones as a framework for probabilistic models to come. It is a formalism...

by search engines to estimate the relevance of documents to a given search query. It is based on the probabilistic retrieval framework developed in the...

A graphical model or probabilistic graphical model (PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional...

equivalent formalisms, including Markov chains, denoising diffusion probabilistic models, noise conditioned score networks, and stochastic differential equations...

given query. Probabilistic theorems like Bayes' theorem are often used in these models. Binary Independence Model Probabilistic relevance model on which is...

technologist Vyvyan Evans mapped out the role of probabilistic context-free grammar (PCFG) in enabling NLP to model cognitive patterns and generate human like...

Quiñonero (2004). "Sparse Probabilistic Linear Models and the RVM". Learning with Uncertainty - Gaussian Processes and Relevance Vector Machines (PDF) (Ph...

many queries. IR models can be broadly divided into three types: Boolean models or BIR, Vector Space Models, and Probabilistic Models. Various comparisons...

In machine learning, a probabilistic classifier is a classifier that is able to predict, given an observation of an input, a probability distribution...

not only probabilistic inference problems but also decision making problems (following the maximum expected utility criterion) can be modeled and solved...

information science, the binary independence model (BIM) is a probabilistic information retrieval technique. The model makes some simple assumptions to make...

held-out calibration set to minimize the calibration loss. Relevance vector machine: probabilistic alternative to the support vector machine See sign function...

content for large language models (LLMs) and other AI systems. AIO focuses on aligning content with the semantic, probabilistic, and contextual mechanisms...

as discriminative models, the reinterpretation here as a probabilistic flow allows also the design of generative calibration models based on this transform...

networks, dimensionality reduction on the word co-occurrence matrix, probabilistic models, explainable knowledge base method, and explicit representation in...

with applications to statistical estimation for probabilistic functions of Markov processes and to a model for ecology". Bulletin of the American Mathematical...

search engines as a central tool in scoring and ranking a document's relevance given a user query. One of the simplest ranking functions is computed...

Diederik P. Kingma and Max Welling. It is part of the families of probabilistic graphical models and variational Bayesian methods. In addition to being seen...

known for his work on probabilistic information retrieval together with Karen Spärck Jones and the Okapi BM25 weighting model. Robertson was born in...

using the term situation model (in their book Strategies of Discourse Comprehension, 1983), showed the relevance of mental models for the production and...

perceptrons and other models that were later found to be reinventions of the generalised linear models of statistics. Probabilistic reasoning was also employed...

computer vision. CRFs are a type of discriminative undirected probabilistic graphical model. Lafferty, McCallum and Pereira define a CRF on observations...

replace the covering law model's inductive-statistical model (IS model), Salmon introduced the statistical-relevance model (SR model), and proposed the requirement...

the DN model emphasized maximal specificity for relevance of the conditions and axioms stated. Together with Hempel's inductive-statistical model, the DN...

Vincent, Pascal; Janvin, Christian (1 March 2003). "A neural probabilistic language model". The Journal of Machine Learning Research. 3: 1137–1155 – via...

6499422. ISSN 2168-1740. Maron, Melvin E.; Kuhns, J. L. (1960). "On relevance, probabilistic indexing, and information retrieval". Journal of the ACM. 7 (3):...

data is used by a learning algorithm to produce a ranking model which computes the relevance of documents for actual queries. Typically, users expect a...

1177/0165551506065787. S2CID 7265523. Maron, M. E. and Kuhns, J. L. 1960. On Relevance, Probabilistic Indexing and Information Retrieval. Journal of the ACM 7, 3, 216–244...

cosmological constant based on probabilistic arguments. Other attempts[which?] have been made to apply similar reasoning to models of particle physics. Such...

McLean Sloughter; Tilmann Gneiting, ensembleBMA: Probabilistic Forecasting using Ensembles and Bayesian Model Averaging, Wikidata Q98972500 Adrian Raftery;...