• BNT

  • Referenced in 69 articles [sw07384]
  • nodes (probability distributions), exact and approximate inference, parameter and structure learning, and static and dynamic...
  • LibDAI

  • Referenced in 15 articles [sw06422]
  • partition sum, marginal probability distributions and maximum probability states. Parameter learning is also supported...
  • MEBN

  • Referenced in 15 articles [sw02784]
  • joint probability distribution over possibly unbounded numbers of hypotheses, and uses Bayesian learning to refine ... emerging collection of highly expressive probability-based languages...
  • kLog

  • Referenced in 4 articles [sw10403]
  • statistical relational learning. Unlike standard approaches, kLog does not represent a probability distribution directly...
  • Pyro

  • Referenced in 9 articles [sw27079]
  • modeling, unifying the best of modern deep learning and Bayesian modeling. It was designed with ... Universal: Pyro can represent any computable probability distribution. Scalable: Pyro scales to large data sets...
  • ARock

  • Referenced in 24 articles [sw16800]
  • fixed point, then with probability one, ARock generates a sequence that converges to a fixed ... systems, convex optimization, and machine learning, as well as distributed and decentralized consensus problems. Numerical...
  • Distributome

  • Referenced in 1 article [sw16209]
  • Interactive Web-based Resource for Probability Distributions. The Probability Distributome Project is an open-source ... exploring, discovering, navigating, learning, and computational utilization of diverse probability distributions. A probability distribution...
  • DPPy

  • Referenced in 5 articles [sw27047]
  • Determinantal point processes (DPPs) are specific probability distributions over clouds of points that are used ... computational tools across physics, probability, statistics, and more recently machine learning. Sampling from DPPs...
  • SOCR

  • Referenced in 12 articles [sw16634]
  • based framework for: interactive distribution modeling, virtual online probability experimentation, statistical data analysis, visualization ... many of our undergraduate and graduate probability and statistics courses and have evidence that SOCR ... build student’s intuition and enhance their learning...
  • ISDEvaluation

  • Referenced in 1 article [sw35485]
  • model of a patient’s individual survival distribution can help determine the appropriate treatment ... probabilities, single-time probability models (for instance the Gail model, predicting 5 year probability) only ... tools that can learn a model that provides an individual survival distribution for each subject ... which gives survival probabilities across all times, such as extensions to the Cox model, Accelerated...
  • ARTMAP

  • Referenced in 5 articles [sw03013]
  • representing a category using a multidimensional Gaussian distribution, 2) allowing a category to grow ... hypervolume, 4) using Bayes’ decision theory for learning and inference, and 5) em- ploying ... addition, the BA estimates the class posterior probability and thereby enables the introduction of loss...
  • pomegranate

  • Referenced in 1 article [sw26684]
  • present pomegranate, an open source machine learning package for probabilistic modeling in Python. Probabilistic modeling ... methods that explicitly describe uncertainty using probability distributions. Three widely used probabilistic models implemented ... models. This approach trivially enables many useful learning strategies, such as out-of-core learning...
  • SoilGrids250m

  • Referenced in 2 articles [sw27798]
  • predictions of depth to bedrock and distribution of soil classes based on the World Reference ... used to fit an ensemble of machine learning methods—random forest and gradient boosting and/or ... attributed to: (1) the use of machine learning instead of linear regression, (2) to considerable ... input uncertainties and derivation of posterior probability distributions (per pixel), and further automation of spatial...
  • HDoutliers

  • Referenced in 0 articles [sw15583]
  • machine learning papers, HDoutliers is based on a distributional model that uses probabilities to determine...
  • RllvmCompile

  • Referenced in 3 articles [sw14511]
  • fits-all approach to compiling R is probably too restrictive. Furthermore, we want ... having to recompile all of R or learn a new compilation framework tha is specific ... should also do the same. We have learned that a centralized code source that requires ... core group to make all ”official” and ”distributed” changes limits innovation (but does improve stability...
  • PredictiveRegression

  • Referenced in 3 articles [sw11352]
  • classical prediction intervals guarantee that the probability of error is equal to the nominal significance ... independent and identically distributed, is popular in machine learning but greatly underused in the statistical...
  • Statiscope

  • Referenced in 1 article [sw25935]
  • data over the Internet. Charts included: Distribution, Probability mass, Density, Box plot, Stem & leaf. Other ... scale. It is intended both for people learning the concepts of statistics and for practical...
  • TransT

  • Referenced in 2 articles [sw34445]
  • Graph Completion. Knowledge graph completion with representation learning predicts new entity-relation triples from ... capture prior distributions of entities and relations. With the type-based prior distributions, our approach ... different contexts and estimates the posterior probability of entity and relation prediction. Extensive experiments show...
  • UNIPASS

  • Referenced in 1 article [sw17675]
  • uncertainties, defining variable probability distribution and probabilistic response models, computing probabilities, identifying most likely outcomes ... into most existing environments without a significant learning curve or additional hardware investment...
  • EAQR

  • Referenced in 1 article [sw27641]
  • design a multiagent reinforcement learning algorithm for cooperative tasks where multiple agents need to coordinate ... performance. In EAQR, Q-value represents the probability of getting the maximal reward, while each ... pushing, and the other is the distributed sensor network problem. Experimental results show that EAQR...