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A novel rejection sampling scheme for posterior probability distributions
IEEE International Conference on Acoustics, Speech, and Signal Processing, 2009Rejection sampling (RS) is a well-known method to draw from arbitrary target probability distributions, which has important applications by itself or as a building block for more sophisticated Monte Carlo techniques.
Luca Martino, J. Míguez
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Ensemble Confidence Estimates Posterior Probability
2005We have previously introduced the Learn++ algorithm that provides surprisingly promising performance for incremental learning as well as data fusion applications. In this contribution we show that the algorithm can also be used to estimate the posterior probability, or the confidence of its decision on each test instance.
Michael Muhlbaier +2 more
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GNSS integer ambiguity posterior probability calculation with controllable accuracy
Journal of Geodesy, 2022Z. Wu
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Beyond the Probability Map - Representation of Posterior Facies Probability
Proceedings, 2014Geologic facies distributions are commonly represented in geomodels by categorical variables that are intrinsically non-Gaussian and thus difficult to calibrate in ensemble Kalman filter-like algorithms. For certain types of stochastic models such as the truncated plurigaussian, it is possible to directly update model variables in such a way that the ...
Y. Zhang, D.S. Oliver, Y. Chen
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Posterior Probabilities for a Consensus Ordering
Psychometrika, 1990In the situation where subjects independently rank order a fixed set of items, the idea of a consensus ordering of the items is defined and employed as a parameter in a class of probability models for rankings. In the context of such models, which generalize those of Mallows, posterior probabilities may be easily formed about the population consensus ...
Michael A. Fligner, Joseph S. Verducci
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Posterior probability of correct selection
Communications in Statistics - Theory and Methods, 1990The selection of the “best” of a number of treatments is studied from a Bayesian point of view using normal likelihoods and priors. The posterior probability of correct selection is expressed as a function of the prior variance and is estimated using a parametric empirical Bayes' approach.
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Bayesian inference and posterior probability maps
Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02., 2004This paper describes the construction of posterior probability maps that enable conditional or Bayesian inferences about regionally-specific effects in neuroimaging. Posterior probability maps are images of the probability or confidence that an activation or effect exceeds some specified threshold, given the data.
K.J. Friston, W. Penny
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Posterior probabilities for choosing a regression model
Biometrika, 1978SUMMARY A simple Bayesian formula for the posterior probability of one of several regression models is shown to be systematically misleading unless all models have the same number of para- meters. Even in this case the use of improper priors leads to arbitrary inferences, as it does more generally.
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Posterior probability measure for image matching
Pattern Recognition, 2008Template matching is one of the principle techniques in visual tracking. Various similarity measures have been developed to find the target in an acquired image by matching with a template. However, mismatching or misidentification may sporadically occur due to the influence of the background pixels included in the designated target model.
Jiang, Ping +3 more
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