gelman bayesian data analysis

Code test Environment: hadoop2.4+mahout1.0Previous blog: mahout Bayesian algorithm Development Ideas (expansion) 1 and mahout Bayesian algorithm development Ideas (expansion) 2 the Bayesian algorithm in Mahout is analyzed to deal with the numerical data. In the previous two blogs, there was no processing of how to clas

1. Preparation:(1) Prior probability: Based on past experience and analysis of the probability, that is, the usual probability, in the full probability of the expression is "from the result of the fruit"(2) Posterior probability: refers to the probability of re-correcting after obtaining the "result" information, usually the conditional probability (but not all of the conditional probability is the posterior probability), in the

Conditional probability: P (x| YJoint probability: P (X, Y)Edge probability: P (X), P (Y).Joint probability = conditional probability * Edge probabilityThe inverse problem is usually solved with conditional probabilities. Inverse problem refers to the problem that the cause should be reversed from the result; A positive problem is the introduction of results from a cause. The inverse problems are common: Communication: According to the received signal conta

into into, get 1-3 shows:Figure 1-3 returning the data graphAccording to the shape, using the mathematical method to obtain the ROC curve area of 0.9222. Then use Weka to view the tool data, 1-4 shows:Figure 1-4 Weka Return Data。Resources:[1] Data mining using Weka (http://www.cnblogs.com/bluewelkin/p/3538599.html)[2]

independent. Naive Bayesian classification and Bayesian belief network based on Bayesian theorem of posterior probability. Bayesian belief networks allow the definition of class conditional independence between subsets of variables.k Nearest Neighbor taxonomy: distance-based classification algorithm, lazy learning met

Preface: Completely do not understand the data analysis, statistics also forget the almost small white began to learn data analysis. Read the "In-depth data analysis", the data

Bayesian Data Analysis: an actual Example of effect 238 Bayesian reasoning: Summary and discussion. 241 (Workshop) r language 243 Additional reading. 249 Chapter 2: Mathematical manhunt -- Bigfoot and the least person Multiplication equal to 253 11.1 how to average. 253 Simpson (Simpson) paradox. 254 Standard deviatio

data, and creating predictions. The simple point is to find out the same kind of attributes.Microsoft Naive Bayes: The Microsoft Naive Bayes algorithm is a Bayesian theorem-based classification algorithm provided by Microsoft SQL Server Analysis Services that can be used for predictive modeling.These algorithms are supported by a number of underlying algorithms,

article describes the Microsoft Linear regression analysis algorithm, the principle and the Microsoft Neural Network analysis algorithm, just like the focus is not the same, the Microsoft Neural Network algorithm is based on a certain purpose, using the existing data for "probing" analysis, focusing on

(regression, interpolation)2. Convex Optimization (global optimization, local optimality, constrained optimization)3. Integral (numerical integral, Analog integral)4. Symbolic calculation (base, equation, integral, differential)Eighth lecture, Random analysisThe characterization and research of uncertainty is an important aspect of financial research and analysis, and this paper introduces some knowledge of stochastic

to group cases in a dataset into clusters that contain similar characteristics. These groupings are useful when browsing data, identifying exceptions in data, and creating predictions. The simple point is to find out the same kind of attributes.Microsoft Naive Bayes: The Microsoft Naive Bayes algorithm is a Bayesian theorem-based classification algorithm provide

result. flv_d.flv│?? ├│?? │?? ├ class 128. Smote sample generation strategy. flv_d.flv│?? │?? ├ Lesson 129. Text analysis and keyword extraction. flv_d.flv│?? │?? ├ class 130. Similarity calculation. flv_d.flv│?? │?? ├ class 131. News data and mission brief. flv_d.flv│?? │?? ├ class 132. TF-IDF keyword extraction. flv_d.flv│?? │?? └ class 133. LDA modeling. flv_d.flv│?? ├│?? │?? ├ class 134. News classific

The 1th chapter introduces "free related ebook + accompanying code" this chapter first introduces the course is what, what characteristics, can learn what, content arrangement, need what foundation, is suitable to study this course and so on. Then we summarize the data analysis, so that we have a whole understanding of the meaning and function of data

(in the value of risks, credit risk)Nineth Lecture, statistical analysisStatistical analysis is the core of financial data analysis, this talk about the common statistical analysis methods, financial applications and Python implementation. 1. Normality test 2, Portfolio Optimization 3, principal component

Space Data Analysis and R language practicesBasic InformationOriginal Title: Applied spatial data analysis with RAuthor: pebesma, E. J.) Gemel-Rubio (Gómez-Rubio, V .)Translator: Xu Aiping Shu HongPress: Tsinghua University PressISBN: 9787302302353Mounting time:Published on: February 1, January 2013Start: 16Page number