Penalized Score Test for High Dimensional Logistic Regression
... We deal with inference problem for high dimensional logistic regression. The main idea is to give penalized estimator by adding penalty to negative log likelihood function which penalizes all variables except the one we are interested in. It shows that this penalized estimator is a compromise betwee ...
... We deal with inference problem for high dimensional logistic regression. The main idea is to give penalized estimator by adding penalty to negative log likelihood function which penalizes all variables except the one we are interested in. It shows that this penalized estimator is a compromise betwee ...
RECURSIVE BAYESIAN ESTIMATION OF MODELS WITH
... with uniform innovations is defined for this purpose. If also unobservable quantities (states) are considered, the state model with uniform innovations is introduced. An approximation of the posterior probability density for both models is proposed so the estimation can run recursively as required i ...
... with uniform innovations is defined for this purpose. If also unobservable quantities (states) are considered, the state model with uniform innovations is introduced. An approximation of the posterior probability density for both models is proposed so the estimation can run recursively as required i ...
Lecture 7: Introduction to Deep Learning Sanjeev
... • Perceptron = network with single threshold gate. • Backpropagation training algorithm rediscovered independently in many fields starting 1960s. (popularized for Neural net training by Rumelhart, Hinton, Williams 1986) • Neural nets find some uses in 1970s and 1980s. • Achieve human level ability t ...
... • Perceptron = network with single threshold gate. • Backpropagation training algorithm rediscovered independently in many fields starting 1960s. (popularized for Neural net training by Rumelhart, Hinton, Williams 1986) • Neural nets find some uses in 1970s and 1980s. • Achieve human level ability t ...
3.Data mining
... The basic steps of the complete-link algorithm are: 1. Place each instance in its own cluster. Then, compute the distances between these points. 2. Step thorough the sorted list of distances, forming for each distinct threshold value dk a graph of the samples where pairs of samples closer than dk ...
... The basic steps of the complete-link algorithm are: 1. Place each instance in its own cluster. Then, compute the distances between these points. 2. Step thorough the sorted list of distances, forming for each distinct threshold value dk a graph of the samples where pairs of samples closer than dk ...
Expectation–maximization algorithm
In statistics, an expectation–maximization (EM) algorithm is an iterative method for finding maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step.