# 需要导入模块: from torch import nn [as 别名]# 或者: from torch.nn importPoissonNLLLoss[as 别名]defloglikelihood(self, reduction):""" Return the log-likelihood """ifself._distr =='poisson':ifreduction =='none':returnself.poisson_cross_entropyreturnnn.PoissonNLLLoss(reduction=reduction)eli...
KLDivLoss(reduction='sum') else: raise AutodiffCompositionError("Loss type {} not recognized. Loss argument must be a string or function. " "Currently, the recognized loss types are Mean Squared Error, Cross Entropy," " L1 loss, Negative Log Likelihood loss, Poisson Negative Log Likelihood,...
model majordrg = age acadmos minordrg logspend / dist =; probmodel age acadmos minordrg logspend; run; /* Fit Statistics -2 Log Likelihood 8147.9 AIC (smaller is better) 8167.9 AICC (smaller is better) 8167.9 BIC (smaller is better) 8240.5 Parameter Estimates for 'Poisson' Model Standard...
xls, /// cells(b(fmt(3)star) se(fmt(2)par([ ]))) /// starlevels(* 0.1 ** 0.05 *** 0.01) /// stats(ll N_g N, labels(log_likelihood Clusters Observation)) /// keep(1.ou#1.post) legend replace 得到结果 期刊排版 示例3 文献来源 Liu, Y., et al. (2024). How does ...
Log Likelihood for Poisson Regression.Holger Reulen
ThePoisson lossfor regression. Assuming that the response variable y follows Poisson distribution, maximum likelihood is used to estimate the parameters by maximuzing the probability of obtaining the observed data. Its string name is'poisson'. ...
Lognormal lifetimes and likelihood-based inference for flexible cure rate models based on COM-Poisson family 来自 EconPapers 喜欢 0 阅读量: 81 作者: N Balakrishnan,S Pal 摘要: Recently, a new cure rate survival model has been proposed by considering the Conway-Maxwell Poisson distribution as the...
method. It assumes that the log of the conditional mean of the dependent variable follows a linear function of the dependent variables. Assuming that the dependent variable follows a Poisson distribution, the regression parameters can be estimated by maximizing the likelihood of the obtained ...
=== Dep. Variable: y No. Observations: 270 Model: NegativeBinomial Df Residuals: 268 Method: MLE Df Model: 1 Date: Sat, 04 Mar 2017 Pseudo R-squ.: nan Time: 16:22:04 Log-Likelihood: -1742.0 converged: True LL-Null: nan LLR p-value: I naively try also to put method="minimize...
The likelihood of the parameters θ0, …,θD-1 is the probability that the training data was sampled from a distribution with these parameters.The log probability can be viewed as logp(y = yi)The prediction function outputs the expected value of that parameterized Poisson distribution, ...