log-likelihood

问题 What is an easy way to calculate the loglikelihood of any distribution fitted to data? 回答1: Solution by OP. Python has 82 standard distributions which can be found here and in scipy.stats.distributions Suppose you find the parameters such that the probability density function(pdf) fits the data as follows: dist = getattr(stats.stats, 'distribution name') params = dist.fit(data) Then since it is a standard distribution included in the SciPy library, the pdf and logpdf can be found and used

来源： https://stackoverflow.com/questions/59495505/attributeerror-str-object-has-no-attribute-parameters-due-to-new-version-of

问题 I am using the 'bife' package to run the fixed effect logit model in R. However, I cannot compute any goodness-of-fit to measure the model's overall fit given the result I have below. I would appreciate if I can know how to measure the goodness-of-fit given this limited information. I prefer chi-square test but still cannot find a way to implement this either. --------------------------------------------------------------- Fixed effects logit model with analytical bias-correction Estimated

问题 I have a problem regarding the following model, where I want to make inference on μ and tau, u is a known vector and x is the data vector. The log-likelihood is I have a problem writing a log-likelihood in R. x <- c(3.3569,1.9247,3.6156,1.8446,2.2196,6.8194,2.0820,4.1293,0.3609,2.6197) mu <- seq(0,10,length=1000) normal.lik1<-function(theta,x){ u <- c(1,3,0.5,0.2,2,1.7,0.4,1.2,1.1,0.7) mu<-theta[1] tau<-theta[2] n<-length(x) logl <- sapply(c(mu,tau),function(mu,tau){logl<- -0.5*n*log(2*pi) -0

问题 I'm using a logistic regression model in sklearn and I am interested in retrieving the log likelihood for such a model, so to perform an ordinary likelihood ratio test as suggested here. The model is using the log loss as scoring rule. In the documentation, the log loss is defined "as the negative log-likelihood of the true labels given a probabilistic classifier’s predictions" . However, the value is always positive, whereas the log likelihood should be negative. As an example: from sklearn