Global optimization using Gaussian processes to estimate biological parameters from image data

Gaussian Processes for Global Optimization

In this paper we frame global optimization as a sequential decision problem. Imagine that we have an unknown and expensive-to-evaluate objective function y(x) that we seek to minimize. The cost associated with computing y(x) compels us to select the location of each new evaluation very carefully. Our problem’s ultimate task is to return a final point xM in the domain, and we define the loss ass...

Novelty Detection in Biological Data using Gaussian Processes

Using neural network to estimate weibull parameters

As is well known, estimating parameters of the tree-parameter weibull distribution is a complicated task and sometimes contentious area with several methods vying for recognition. Weibull distribution involves in reliability studies frequently and has many applications in engineering. However estimating the parameters of Weibull distribution is crucial in classical ways. This distribution has t...

Using Excel To Estimate Parameters From Observed Data: An Example From Source Memory Data

We present an application, using ExcelTM, that can solve best-fitting parameters for multinomial models. Multinomial modeling has become increasingly popular and can be used in a variety of domains, such as memory, perception, and other domains in which processes are assumed to be dissociable. We offer an application that can be used for a variety of psychological models and can be used on both...

­­Image Segmentation using Gaussian Mixture Model

Abstract: Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. In this paper, we used Gaussian mixture model to the pixels of an image. The parameters of the model were estimated by EM-algorithm.   In addition pixel labeling corresponded to each pixel of true image was made by Bayes rule. In fact,...