[PDF] Top 20 Proteochemometric modeling in a Bayesian framework

... An additional outcome of GP with respect to SVM is the estimation of the uncertainty of predictions. Indeed, the Bayesian formulation of GP permits to obtain inter- vals of confidence for individual predictions ... See full document

... A core focus of statistics is determining how much of the variation in data may be attributed to the signal of interest, and how much to noise. When the sources of variation are many and complex, a Bayesian ... See full document

... a Bayesian framework, called Bayes- GLMM ...mixed modeling; and (4) model estimation by both Markov chain Monte Carlo sampling and maximal likelihood ...a Bayesian framework for ... See full document

... Modeling the progression of damage in composites materials is a challenge mainly due to the uncertainty in the multi-scale physics of the damage process and the large variability in behavior that is observed, even ... See full document

... Bayesian framework. When studying human psychophysical performance, the Bayesian framework allows the experimenter to test whether this bias is being utilized ...specific Bayesian model ... See full document

... By modeling bias, thus, we mean to impose a prior distribution on the amount of endogeneity of the endogenous vari- ables’ coefficient estimators and then compute the distribution of the associated parameters of ... See full document

... This paper proposed a novel way of dealing with endogeneity bias when there is no additional information such as instrumental or proxy variables. In particular, a prior is imposed on the endogeneity bias and the ... See full document

... including an added average measure for the full binding pocket sequence. The Z-scale descriptors capture the diversity of amino acids as they are the fi rst three uncorrelated components originating from a principal ... See full document

... single framework for explaining two types of judgments that people make about the dynamics of objects, based on Bayesian ...this framework, we define a particular model of dynamics – essentially ... See full document

... a Bayesian approach to the unknown parameters in WinBUGSand apply it in real ...the Bayesian approach is that the latter considers parameters as random variables that are characterized by a prior ... See full document

... We present in this paper a Bayesian CAD system for robotic applications. We address the problem of the propagation of geometric uncertainties and how esian CAD system for robotic applications. We address the ... See full document

... semiparametric Bayesian proportional hazards competing risk models have been introduced by Gelfand and Mallick (1995) in survival ...consider modeling duration of single-home ...the Bayesian ... See full document

... This paper presented a holistic and spatial-explicit ap- proach for assessing malaria risk in Tanzania, taking into account differences in vulnerability and EIR. Multidi- mensional vulnerability was modeled considering ... See full document

... We have described the merging of four sentiment lexicons into a single resource, which we have named SentiMerge. To demonstrate the utility of the combined lexicon, we set up a word-level sentiment clas- sification task ... See full document

... where all coe¢cients are convolutions of the structural parameters in the model included in the vector : Of course the theoretical model imposes some restrictions on the VAR, that can be tested by evaluating them against ... See full document

... on Bayesian application [9] like greedy heuristic, combined with techniques to avoid local maxima in the posterior density ...the Bayesian approaches is the fact that they are relatively ... See full document

... a Bayesian framework that yielded posterior distributions of the NRH parameters and posterior predictions of GPP and its associ- ated uncertainty (see Raj et ... See full document

... variance of the probability distribution assessed by the Bayesian estimation (B-numer or B-ln) is about six-times smaller than the variance of the generic distribution. This is reasonable, as specific information ... See full document

... Gaussian process (GP) prior provides a flexible building block for many hierarchical Bayesian mod- els (Rasmussen and Williams, 2006). GPstuff (v4.1) is a versatile collection of computational tools for GP models ... See full document

... using Bayesian techniques in implementing a network-based IDS that can tell us a computer process is normal (harmless) or abnormal ...network. Bayesian methods utilize a search-and-source procedure to ... See full document