Titlebook: Statistical Methods for Data Analysis; With Applications in Luca Lista Book 2023Latest edition The Editor(s) (if applicable) and The Author

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书目名称Statistical Methods for Data Analysis影响因子(影响力)




书目名称Statistical Methods for Data Analysis影响因子(影响力)学科排名




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书目名称Statistical Methods for Data Analysis网络公开度学科排名




书目名称Statistical Methods for Data Analysis被引频次




书目名称Statistical Methods for Data Analysis被引频次学科排名




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书目名称Statistical Methods for Data Analysis年度引用学科排名




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书目名称Statistical Methods for Data Analysis读者反馈学科排名

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Introduction to Probability and Inference,ity are introduced: classical probability, frequentist and Bayesian approaches, that are more extensively discussed in dedicated chapters. The problem to generalize classical probability to the continuum is discussed, and the axiomatic approach to probability due to Kolmogorov is introduced. The gen

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Discrete Probability Distributions,statistical indicators: average, variance, etc. The problem of determining the probability distribution under variable transformation is presented. The most frequently used discrete distribution are introduced: Bernoulli, binomial, multinomial and Poisson, and their properties are discussed. The law

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Random Numbers and Monte Carlo Methods,o a chaotic and poorly predictable regime. The main methods to extract pseudorandom numbers distributed according to the desired density function are presented. Monte Carlo methods are introduced, in particular, the hit-or-miss Monte Carlo and importance sampling. The use of Monte Carlo method for n

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Bayesian Probability and Inference,ior and posterior probabilities are defined. The application to the continuous case is presented, and Bayesian inference is introduced, which can also be interpreted as learning process from multiple observations. The treatment of nuisance parameters with Bayesian inference is discussed. Credible in

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Frequentist Probability and Inference,hod is maximum likelihood, which is introduced, and its properties are discussed. Best fits using binned and unbinned distributions are presented with examples for different typical cases. Different approaches to estimate uncertainty intervals with maximum likelihood estimates are introduced. The ex

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Combining Measurements,measurements compared to individual ones. Different approaches are presented. When possible, if the complete likelihood function can be determined, a simultaneous fit to the different data sets provides the best combined estimate. If the likelihood function cannot be determined with sufficient accur

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