Exponential and bayesian conjugate families: Review and extensions

Summary The notion of a conjugate family of distributions plays a very important role in the Bayesian approach to parametric inference. One of the main features of such a family is that it is closed under sampling, but a conjugate family often provides prior distributions which are tractable in various other respects. This paper is concerned with the properties of conjugate families for exponential family models. Special attention is given to the class of natural exponential families having a quadratic variance function, for which the theory is particularly fruitful. Several classes of conjugate families have been considered in the literature and here we describe some of their most interesting features. Relationships between such classes are also discussed. Our aim is to provide a unified approach to the theory of conjugate families for exponential family likelihoods. An important aspect of the theory concerns reparameterisations of the exponential family under consideration. We briefly review the concept of a conjugate parameterisation, which provides further insight into many of the properties discussed throughout the paper. Finally, further implications of these results for Bayesian conjugate analysis of exponential families are investigated. Research partially funded by ONR Grant N00014-96-1-0192 Research funded by NSF Grant DMS 94.04408 and Texas ARP Grant 003658130     

Thresholding based on variance and intensity contrast

A new thresholding criterion is formulated for segmenting small objects by exploring the knowledge about intensity contrast. It is the weighted sum of within-class variance and intensity contrast between the object and background. Theoretical bounds of the weight are given for the uniformly distributed background and object, followed by the procedure to estimate the weight from prior knowledge. Tests against two real and two synthetic images show that small objects can be extracted successfully irrespective of the complexity of background and difference in class sizes.     

分组样本下先验BN模型及条件概率的学习算法

文章扩展经典的先验BN模型，采用两层学习结构讨论分组样本下BN模型的条件概率及学习算法：一层是对各组私有条件概率分布的学习；另一层是对各组公有条件概率分布的学习。算法在综合公有后验条件概率分布和本组学习实例数据分布特征的基础上，实现对各组私有条件概率分布的学习，并可通过经验或学习来改变综合值中共性和个性的比例。     

汾河二库碾压混凝土VC值的事前控制

本文分析了影响汾河二库碾压混凝土VC值的主要因素，总结出VC值与气温、相对湿度、停滞时间的定量关系，提出了VC值的事前控制办法。     

A method for assessing the worth of prior knowledge about the measurand

A plausible method is proposed that allows assessment of the worth of prior knowledge about a measurand θ relative to the information contained in the measurement data x. It takes a Bayesian point of view, i.e. θ as well as x is considered to be a random variable. The worth of prior knowledge about θ is defined as that number of v of individual measurements in x, for which the influences of the prior information in the worst case and of the data x via the likelihood function are equal. v can be interpreted as the number of individual measurements to which the prior information is at least equivalent. The method is quite universal, because it does not assume any particular estimator x) and because it takes into account the many different forms prior information about θ can have.     

Retrieval of canopy biophysical variables from bidirectional reflectance: Using prior information to solve the ill-posed inverse problem

Estimation of canopy biophysical variables from remote sensing data was investigated using radiative transfer model inversion. Measurement and model uncertainties make the inverse problem ill posed, inducing difficulties and inaccuracies in the search for the solution. This study focuses on the use of prior information to reduce the uncertainties associated to the estimation of canopy biophysical variables in the radiative transfer model inversion process. For this purpose, lookup table (LUT), quasi-Newton algorithm (QNT), and neural network (NNT) inversion techniques were adapted to account for prior information. Results were evaluated over simulated reflectance data sets that allow a detailed analysis of the effect of measurement and model uncertainties. Results demonstrate that the use of prior information significantly improves canopy biophysical variables estimation. LUT and QNT are sensitive to model uncertainties. Conversely, NNT techniques are generally less accurate. However, in our conditions, its accuracy is little dependent significantly on modeling or measurement error. We also observed that bias in the reflectance measurements due to miscalibration did not impact very much the accuracy of biophysical estimation.     

Adapting prior knowledge activation: Mobilisation, perspective taking, and learners’ prior knowledge

This study investigates the effects of two prior knowledge activation strategies, namely, mobilisation and perspective taking, on learning. It is hypothesised that the effectiveness of these strategies is influenced by learners’ prior domain knowledge. More specifically, mobilisation is expected to be the most effective activation strategy at lower levels of prior knowledge. Mobilisation is a bottom-up oriented strategy that serves a broad stage-setting function. It provides learners with a relevant context in which new information can be integrated, which might be especially beneficial for learners with lower levels of prior knowledge to help them extend their limited knowledge base. As prior knowledge increases, perspective taking is expected to become the most effective strategy for activating learners’ prior knowledge. Perspective taking is a top-down oriented strategy that results in the activation of a corresponding schema. This schema guides the selection and processing of information relevant to the schema, which might especially support learners with higher levels of prior knowledge to refine their already elaborated knowledge base. The effectiveness of the activation strategies (in terms of learning task performance) was indeed influenced by learners’ prior knowledge in the hypothesised direction.     

A higher-order active contour model of a ‘gas of circles’ and its application to tree crown extraction

We present a model of a ‘gas of circles’: regions in the image domain composed of a unknown number of circles of approximately the same radius. The model has applications to medical, biological, nanotechnological, and remote sensing imaging. The model is constructed using higher-order active contours (HOACs) in order to include non-trivial prior knowledge about region shape without constraining topology. The main theoretical contribution is an analysis of the local minima of the HOAC energy that allows us to guarantee stable circles, fix one of the model parameters, and constrain the rest. We apply the model to tree crown extraction from aerial images of plantations. Numerical experiments both confirm the theoretical analysis and show the empirical importance of the prior shape information.     

Prior language experience and language anxiety as predictors for non-native language commercial website use intention

Based on the research model, language anxiety, prior non-native language experience, Internet self-efficacy and language self-efficacy are analyzed for the intention to use non-native language commercial web sites, respectively. Prior non-native language experience has affected language anxiety, language self-efficacy and intention to use non-native language commercial web sites, respectively. By the same token, whether or not Internet self-efficacy and language self-efficacy affected by language anxiety is also examined. A valid sample of 418 undergraduates was tested in this study. Regression analysis results fully supported the model tested. These results suggest that language anxiety, prior non-native language experience, language self-efficacy and Internet self-efficacy have an effect on the intention to use non-native language commercial web sites. Prior non-native language experience has significantly affected language anxiety, language self-efficacy and the intention to use the non-native language commercial web sites, respectively. Furthermore, language anxiety has significantly affected language self-efficacy and Internet self-efficacy, respectively. Educational research and practitioner implications are provided at the end of the paper.     

结合背景光融合及水下暗通道先验和色彩平衡的#br# 水下图像增强

水下环境、光线衰减和拍摄方式造成水下图像具有不同色调、对比度和模糊度.基于图像成像模型的水下图像复原方法通常基于暗通道先验或最大像素先验,容易受到水下复杂环境的干扰而输出低质量的复原图像,因此文中提出基于背景光融合及水下暗通道先验和色彩平衡的水下图像增强方法.首先,提出多候选背景光融合方法,估计正确的背景光.然后,基于高质量水下图像统计得出水下暗通道先验,计算更准确的RGB分量传输地图.将复原图像从RGB颜色模型转换到CIE-Lab颜色模型,对L亮度分量和a、b色彩分量分别进行归一化拉伸和优化调整,进一步提高复原后水下图像的亮度和对比度.多种定性和定量分析说明文中方法增强的图像在对比度、亮度和颜色上的显示效果优于大部分现有的水下图像增强方法复原的图像.