Gaussian process priors can be used to define flexible, probabilistic classification models. Unfortunately exact Bayesian inference is analytically intractable and various approximation techniques have been proposed. In this work we review and compare Laplace’s method and Expectation Propagation for approximate Bayesian inference in the binary Gaussian process classification model. We present a...

We propose a generative mixture model classifier that allows for the class conditional densities to be represented by mixtures having certain subsets of their components shared or common among classes. We argue that, when the total number of mixture components is kept fixed, the most efficient classification model is obtained by appropriately determining the sharing of components among class co...

In this study, job expectations of X and Y generation teachers were tried to explained. This study is done by using “mixed search” model in which quantitative and qualitative techniques are used together. In this study, 218 participants took place and researchers applied “Job Expectation Scale” to these participants. Besides this, the questions of semi-structured “Expectation from the Job Inter...