نتایج جستجو برای: gaussian model

تعداد نتایج: 2151828  

Journal: :iranian journal of radiology 0
mahdi dodangeh somayeh gholami bardeji postdoc zeinab gholami radiology department, shiraz university of medical sciences reza jalli radiology department, shiraz university of medical sciences rezvan ravanfar haghighi sepideh sefidbakht radiology department, shiraz university of medical sciences

conclusions in the case of existence gaussian noise, the results confirm that denoising is not effective on the measurement of t2* value. in the case of image distortion by rician noise, a predictor model is proposed to estimate the original t2* value. the predictor model is used to estimate the t2* value of new patients. the predicted t2* values were in good agreement with the corresponding or...

ژورنال: علوم آب و خاک 2019

The aim of this study was to evaluate the spatial distribution of soil infiltration using geostatistics methods in a regional scale on 400 hectares of Mansour Abad Plain, in Larestan region, Fars Province. Sampling and parameters measurement were done for 78 points in a regular grid with a distance of 100*100 meters; for these variables, the best variogram model between linear, exponential, Gau...

2009
Nicolas Verzelen

We consider the problem of estimating the conditional mean of a real Gaussian variable Y = ∑p i=1 θiXi+ ǫ where the vector of the covariates (Xi)1≤i≤p follows a joint Gaussian distribution. This issue often occurs when one aims at estimating the graph or the distribution of a Gaussian graphical model. We introduce a general model selection procedure which is based on the minimization of a penal...

پایان نامه :وزارت علوم، تحقیقات و فناوری - دانشگاه علامه طباطبایی 1390

over the past decades a number of approaches have been applied for forecasting mortality. in 1992, a new method for long-run forecast of the level and age pattern of mortality was published by lee and carter. this method was welcomed by many authors so it was extended through a wider class of generalized, parametric and nonlinear model. this model represents one of the most influential recent d...

2013
Ming Yang Yingming Li Zhongfei Zhang

In this paper, we study the multi-task learning problem with a new perspective of considering the structure of the residue error matrix and the low-rank approximation to the task covariance matrix simultaneously. In particular, we first introduce the Matrix Generalized Inverse Gaussian (MGIG) prior and define a Gaussian Matrix Generalized Inverse Gaussian (GMGIG) model for low-rank approximatio...

A two-factor experiment with interaction between factors wherein observations follow an Inverse Gaussian model is considered. Analysis of the experiment is approached via an empirical Bayes procedure. The conjugate family of prior distributions is considered. Bayes and empirical Bayes estimators are derived. Application of the procedure is illustrated on a data set, which has previously been an...

1999
Carl E. Rasmussen

In a Bayesian mixture model it is not necessary a priori to limit the number of components to be finite. In this paper an infinite Gaussian mixture model is presented which neatly sidesteps the difficult problem of finding the “right” number of mixture components. Inference in the model is done using an efficient parameter-free Markov Chain that relies entirely on Gibbs sampling.

2017
Tom Diethe Niall Twomey Peter Flach

Consider the situation where we have some pre-trained classification models for bike rental stations (or any other spatially located data). Given a new rental station (deployment context), we imagine that there might be some rental stations that are more similar to this station in terms of the daily usage patterns, whether or not these stations are close by or not. We propose to use a Gaussian ...

2017

Unsupervised anomaly detection on multior high-dimensional data is of great importance in both fundamental machine learning research and industrial applications, for which density estimation lies at the core. Although previous approaches based on dimensionality reduction followed by density estimation have made fruitful progress, they mainly suffer from decoupled model learning with inconsisten...

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