Priors for the Parameters
Hierarchical Priors for Bias Parameters in Bayesian Sensitivity Analysis for Unmeasured Confounding
... dimensional parameters that model competing bias ...default priors for bias parameters for observational studies with binary ...Hierarchical priors constrain the magnitude and direction of ...
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Bayesian Estimation for Parameters of Power Function Distribution under Various Priors
... Abstract Although the idea of Bayesian inference dates back to the late 18th century, its use by statisticians has been rare until recently. But due to advancement in the simulation techniques Bayesian inference and ...
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Using IRT parameters as informative priors in second-order Bayesian latent growth modeling
... informative priors are used for item ...informative priors would improve convergence rates; this may be due to a potential scaling problem, discussed ...
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Incorporating priors on expert performance parameters for segmentation validation and label fusion: a maximum a posteriori STAPLE.
... We have aligned these images in a common template using Guimond et al.’s method [12], and run STAPLE first without taking into account missing struc- tures (regular STAPLE algorithm as proposed in [5]). Then, we have run ...
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Regression modelling using priors depending on Fisher information covariance kernels (I priors)
... The classical ANOVA decomposition The standard one-way ANOVA is essentially a linear regression model which allows comparison of means from two or more samples. Given sets of observations y j = { y 1j , . . . , y n j j } ...
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Multiple priors and no transaction region
... degenerate price conditions, similar to those that were obtained by Dow and Werlang (1992) and Carlier and Dana (2003) within the CEU theory under the distorted prob- ability. Having incorporated proportional transaction ...
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Transformational Priors Over Grammars
... reasonable priors over PCFGs. To estimate the parameters is to discover a notion of relatedness among context-free rules such that related rules tend to have related ...
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Multiple Priors And No-Transaction Region
... Having incorporated proportional transaction costs we have seen that the non- additivity of the investor’s preferences has an impact on an optimal policy of the investor. As in the case of standard Expected Utility ...
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Common priors for generalized type spaces
... the parameters of the modeled situation, to which we also refer as the types of the nature, are given by a parameter set (Mertens and Zamir, 1985; Brandenburger and Dekel, 1993; Heifetz, ...
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Weight Priors for Learning Identity Relations
... For the task of learning identity relations, we generate synthetic data and use a standard feed-forward neural network. The input vector is binary and the target values are [0, 1] for unequal and [1, 0] for equal vector ...
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fMRI activation detection with EEG priors
... activation parameters in the form of selection indicators for the stimulus component and inspected the size of corresponding selection probability estimates, which serve as activation probability ...
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Priors in Bayesian Learning of Phonological Rules
... 1 Introduction Unsupervised learning presents unusual challenges to the field of computational linguistics. In super- vised systems, the task of learning can often be re- stricted to finding the optimal values for the ...
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Benchmark priors for Bayesian models averaging
... improper priors for parameters that are common to all models, and partly on a g-prior structure as in Zellner ...the priors proposed in the literature, the prior we propose does not violate the rules ...
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Exponential Priors for Maximum Entropy Models
... those parameters (λ’s) that had at least 35 training instances – enough to typically overcome the prior and train the parameter re- ...model parameters, and yet this seems like a good way to get inspiration ...
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Regularizing Priors for Linear Inverse Problems
... and parameters because they consider a finite dimensional projection of (1) and recover x only on a grid of points, see Chapter 5 in Kaipio and Somersalo (2004) and Helin (2009), Lassas et ...
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Diffusion-based spatial priors for imaging
... matrix is the Green Ts function of a diffusive process, i.e., a diffusion kernel, which encodes the solution of a diffusion equation involving a weighted graph Laplacian. This has the advantage of providing a full ...
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Self-Locating Priors and Cosmological Measures
... continuous parameters (perhaps having to do with one’s mental state), in which case it is plausible that reasonable priors will assign its existential generalisation probability zero, even conditional on ...
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Benchmark Priors for Bayesian Model Averaging
... the parameters within each ...“non-informative” priors does not work in this situation, since improper priors can not be used for model-specific parameters [attempts to overcome this include ...
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On information, priors, econometrics, and economic modeling
... Let us examine the behavior of an individual who learns about the parame- ters of her/his utility function under inflation. If we think of the parameters as random variables, then the information gained from ...
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A Comment on Priors for Bayesian Occupancy Models
... these priors can impact inference on the habitat factors influ- encing ...about parameters than is given when solely reporting the 95% credible ...t priors more closely approximated the MLEs than ...
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