Results 121 to 130 of about 494,632 (168)

The Distribution of Posterior Malleolus Fracture Lines

Foot & Ankle International, 2021
Background: The morphology and classification of posterior malleolus (PM) fractures remain controversial. An increasing number of studies have found that merely focusing on the fragment size does not lead to a satisfactory prognosis.
Yuan Quan, Hao Lu, Hailin Xu, Yijun Liu, Wenyong Xie, Boyu Zhang, Dianying Zhang, Baoguo Jiang   +7 more
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Approximate Posterior Distributions

Journal of the American Statistical Association, 1976
Abstract This paper proposes the use of approximate posterior distributions resulting from operational prior distributions chosen with regard to the realized likelihood function. L.J. Savage's “precise measurement” is generalized for approximation in terms of an arbitrary operational prior density, including mixed-type prior distributions with positive
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Deriving the Posterior Distribution

2020
In the preceding chapters, we discussed how we can assign a prior distribution for our parameters, and how to choose a likelihood function that captures the information content of our data. So all that is left, is to apply Bayes’ Theorem (Eq. ( 1.2)) to derive our desired posterior distribution.
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The Balanced Accuracy and Its Posterior Distribution

2010 20th International Conference on Pattern Recognition, 2010
Evaluating the performance of a classification algorithm critically requires a measure of the degree to which unseen examples have been identified with their correct class labels. In practice, generalizability is frequently estimated by averaging the accuracies obtained on individual cross-validation folds.
Kay Henning Brodersen, Cheng Soon Ong, Klaas Enno Stephan, Joachim M. Buhmann   +3 more
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Expected Distributions of Posterior Distributions

_ When statistical analyses are performed for probabilistic events, the results vary correspondingly to random fluctuations of the data. Results of Bayesian analyses are given by posterior distributions of parameters. This study investigated behaviors of posterior distributions by expected posterior distributions, which is given by averaging posterior ...
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Deriving Posterior Distributions

2002
Density (5.7) can not be expressed in closed form or at least approximated for the general case (for example by linearization), when a closed form representation of y s F (x i,m i ) is missing. This is exactly the case with a DEA estimated frontier. So parameter estimation based on analytical evaluation of this likelihood in applied work (with unknown ...
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Some posterior distributions for the normal mean

International Journal of Computer Mathematics, 2011
Two posterior distributions for the mean of the normal distribution are obtained by deriving the distributions of the product XY and the ratio X/Y when X and Y are normal and Student's t random variables distributed independently of each other. Estimation of the associated credible intervals is considered and computer programs are provided for ...
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Force distribution in splinted posterior teeth

Oral Surgery, Oral Medicine, Oral Pathology, 1957
Summary A force analysis of some of the factors involved in splinting periodontally involved posterior teeth has been presented. The root anatomy of the abutment teeth may require changes in the recommended splint. The differences in force distribution between maxillary and mandibular straight and curved arch splints has been analyzed.
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Posterior Predictive Distribution

2015
The posterior predictive distribution is the distribution of future observations, conditioned on the information available from existing observations. It is the main Bayesian tool for treating predictive problems in statistics. We define the posterior predictive distribution and illustrate its main features in Bayesian parametric inference.
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Posterior Distributions and Inference

2007
The first section of this chapter discusses general properties of posterior distributions. It continues with an explanation of how a Bayesian statistician uses the posterior distribution to conduct statistical inference, which is concerned with learning about parameter values either in the form of point or interval estimates, making predictions, and ...
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