{"product_id":"introduction-to-statistical-modelling-and-inference","title":"Introduction to Statistical Modelling and Inference","description":"\u003cp\u003eThe complexity of large-scale data sets (“Big Data”) has stimulated the development of advanced \u003cbr\u003ecomputational methods for analysing them. There are two different kinds of methods to aid this. The \u003cbr\u003emodel-based method uses probability models and likelihood and Bayesian theory, while the model-free \u003cbr\u003emethod does not require a probability model, likelihood or Bayesian theory. These two approaches \u003cbr\u003eare based on different philosophical principles of probability theory, espoused by the famous \u003cbr\u003estatisticians Ronald Fisher and Jerzy Neyman.\u003cbr\u003eIntroduction to Statistical Modelling and Inference covers simple experimental and survey designs, \u003cbr\u003eand probability models up to and including generalised linear (regression) models and some \u003cbr\u003eextensions of these, including finite mixtures. A wide range of examples from different application \u003cbr\u003efields are also discussed and analysed. No special software is used, beyond that needed for maximum \u003cbr\u003elikelihood analysis of generalised linear models. Students are expected to have a basic \u003cbr\u003emathematical background in algebra, coordinate geometry and calculus.\u003cbr\u003eFeatures\u003cbr\u003e• Probability models are developed from the shape of the sample empirical cumulative distribution \u003cbr\u003efunction (cdf) or a transformation of it.\u003cbr\u003e• Bounds for the value of the population cumulative distribution function are obtained from the \u003cbr\u003eBeta distribution at each point of the empirical cdf.\u003cbr\u003e• Bayes’s theorem is developed from the properties of the screening test for a rare condition.\u003cbr\u003e• The multinomial distribution provides an always-true model for any randomly sampled data.\u003cbr\u003e• The model-free bootstrap method for finding the precision of a sample estimate has a model-based \u003cbr\u003eparallel – the Bayesian bootstrap – based on the always-true multinomial distribution.\u003cbr\u003e• The Bayesian posterior distributions of model parameters can be obtained from the maximum \u003cbr\u003elikelihood analysis of the model.\u003c\/p\u003e\u003cp\u003eThis book is aimed at students in a wide range of disciplines including Data Science. The book is \u003cbr\u003ebased on the model-based theory, used widely by scientists in many fields, and compares it, in less \u003cbr\u003edetail, with the model-free theory, popular in computer science, machine learning and official \u003cbr\u003esurvey analysis. The development of the model-based theory is accelerated by recent developments\u003cbr\u003ein Bayesian analysis.\u003c\/p\u003e","brand":"Taylor \u0026 Francis Ltd","offers":[{"title":"Default Title","offer_id":54238083383640,"sku":"9781032105710","price":107.99,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0278\/1295\/4195\/files\/9781032105710_f04a6efc-ff67-47e5-8038-86542e007404.jpg?v=1777556733","url":"https:\/\/agendabookshop.com\/products\/introduction-to-statistical-modelling-and-inference","provider":"Agenda Bookshop","version":"1.0","type":"link"}