Bayesian logistic regression 一个是 spike and slab prior,一个是 exploration and exploitation(也称 E&E)。 spike and slab prior 这其实是 Bayesian 领域里面的某种奇怪的先验,比如我们常见的 case,Poisson-Gamma 这个 Bayesian model,选用 Poisson distribution 计数,而 Gamma distribution 作为 conjugate prior 是因...
判别模型(discriminative model)通过求解条件概率分布P(y|x)或者直接计算y的值来预测y。 线性回归(Linear Regression),逻辑回归(Logistic Regression),支持向量机(SVM), 传统神经网络(Traditional Neural Networks),线性判别分析(Linear Discriminative Analysis),条件随机场(Conditional Random Field)、感知机、决策树、KNN...
map_estimate = pm.find_MAP(model=iris_classify) print(map_estimate) 其中train是训练集的pandas dataframe。sepal_length,sepal_width,petal_length,petal_width是四列features。 第一行,先创建一个Model叫iris_classify 第二行,建立一个叫priors_iris的字典,作为先验。字典的key是4个features+1个Bias。每个key...
I'm attempting to create a three-level logistic regression model in pymc3. There is a top level, mid level, and an individual level, where the mid-level coefficients are estimated from top-level coefficients. I'm having difficulty specifying the proper data structure for the mid level, howev...
model { vector < lower = 0.001 > [N] mu; vector < lower = 1.001 > [N] rv; # priors r !cauchy(0, 1); beta !pareto(1, 1.5); # vectorize the overdispersion for(nin1:N) { rv[n] < - square(r + 1) - 1; } # regression ...
This example shows how to use the slice sampler as part of a Bayesian analysis of the mileage test logistic regression model, including generating a random sample from the posterior distribution for the model parameters, analyzing the output of the sampler, and making inferences about the model pa...
Risk of the event is usually modeled using a logistic regression model, with a random intercept to control for heterogeneity among clusters. Model specification requires to decide which regressors have a non-negligible effect, and hence, should be included in the final model and whether risk is ...
[ML] Bayesian Logistic Regression 简单概率分类 Ref: 逻辑回归与朴素贝叶斯有什么区别? Ref: 机器学习笔记——逻辑回归(对数几率回归)和朴素贝叶斯分类器的对比 首先,搞清楚一个问题。 naive bayes 能分类;逻辑回归也能分类;两者解决问题的角度有何不同? 优化目标不同 逻辑回归:优化的后验likelihood 【这个好理解...
A Generalised Linear Model is a flexible mechanism for extending ordinary linear regression to more general forms of regression, including logistic regression (classification) and Poisson regression (used for count data), as well as linear regression itself. GLMs allow for response variables that have...
判别模型(discriminative model)通过求解条件概率分布P(y|x)或者直接计算y的值来预测y。 线性回归(Linear Regression),逻辑回归(Logistic Regression),支持向量机(SVM), 传统神经网络(Traditional Neural Networks),线性判别分析(Linear Discriminative Analysis),条件随机场(Conditional Random Field)、感知机、决策树、KNN...