{"product_id":"introduction-to-bayesian-statistics-hardback-9781118091562","title":"Introduction to Bayesian Statistics (Hardback) 9781118091562","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eIntroduction to Bayesian Statistics\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eWilliam M. Bolstad (Author), James M. Curran (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781118091562, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 29 November 2016\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e624 pages\u003cbr\u003e23.4 x 15.8 x 3.3 cm, 0.975 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003e\"...this edition is useful and effective in teaching Bayesian inference at both elementary and intermediate levels. It is a well-written book on elementary Bayesian inference, and the material is easily accessible. It is both concise and timely, and provides a good collection of overviews and reviews of important tools used in Bayesian statistical methods.\"\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThere is a strong upsurge in the use of Bayesian methods in applied statistical analysis, yet most introductory statistics texts only present frequentist methods. Bayesian statistics has many important advantages that students should learn about if they are going into fields where statistics will be used. In this third Edition, four newly-added chapters address topics that reflect the rapid advances in the field of Bayesian statistics. The authors continue to provide a Bayesian treatment of introductory statistical topics, such as scientific data gathering, discrete random variables, robust Bayesian methods, and Bayesian approaches to inference for discrete random variables, binomial proportions, Poisson, and normal means, and simple linear regression. In addition, more advanced topics in the field are presented in four new chapters: Bayesian inference for a normal with unknown mean and variance; Bayesian inference for a Multivariate Normal mean vector; Bayesian inference for the Multiple Linear Regression Model; and Computational Bayesian Statistics including Markov Chain Monte Carlo. The inclusion of these topics will facilitate readers' ability to advance from a minimal understanding of Statistics to the ability to tackle topics in more applied, advanced level books. Minitab macros and R functions are available on the book's related website to assist with chapter exercises. \u003ci\u003eIntroduction to Bayesian Statistics, Third Edition \u003c\/i\u003ealso features:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eTopics including the Joint Likelihood function and inference using independent Jeffreys priors and join conjugate prior\u003c\/li\u003e \u003cli\u003eThe cutting-edge topic of computational Bayesian Statistics in a new chapter, with a unique focus on Markov Chain Monte Carlo methods\u003c\/li\u003e \u003cli\u003eExercises throughout the book that have been updated to reflect new applications and the latest software applications\u003c\/li\u003e \u003cli\u003eDetailed appendices that guide readers through the use of R and Minitab software for Bayesian analysis and Monte Carlo simulations, with all related macros available on the book's website\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eIntroduction to Bayesian Statistics, Third Edition \u003c\/i\u003eis a textbook for upper-undergraduate or first-year graduate level courses on introductory statistics course with a Bayesian emphasis. It can also be used as a reference work for statisticians who require a working knowledge of Bayesian statistics.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePreface xiii \u003cp\u003e\u003cb\u003e1 Introduction to Statistical Science 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 The Scientic Method: A Process for Learning 3\u003c\/p\u003e \u003cp\u003e1.2 The Role of Statistics in the Scientic Method 5\u003c\/p\u003e \u003cp\u003e1.3 Main Approaches to Statistics 5\u003c\/p\u003e \u003cp\u003e1.4 Purpose and Organization of This Text 8\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Scientic Data Gathering 13\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Sampling from a Real Population 14\u003c\/p\u003e \u003cp\u003e2.2 Observational Studies and Designed Experiments 17\u003c\/p\u003e \u003cp\u003eMonte Carlo Exercises 23\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Displaying and Summarizing Data 31\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Graphically Displaying a Single Variable 32\u003c\/p\u003e \u003cp\u003e3.2 Graphically Comparing Two Samples 39\u003c\/p\u003e \u003cp\u003e3.3 Measures of Location 41\u003c\/p\u003e \u003cp\u003e3.4 Measures of Spread 44\u003c\/p\u003e \u003cp\u003e3.5 Displaying Relationships Between Two or More Variables 46\u003c\/p\u003e \u003cp\u003e3.6 Measures of Association for Two or More Variables 49\u003c\/p\u003e \u003cp\u003eExercises 52\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Logic, Probability, and Uncertainty 59\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Deductive Logic and Plausible Reasoning 60\u003c\/p\u003e \u003cp\u003e4.2 Probability 62\u003c\/p\u003e \u003cp\u003e4.3 Axioms of Probability 64\u003c\/p\u003e \u003cp\u003e4.4 Joint Probability and Independent Events 65\u003c\/p\u003e \u003cp\u003e4.5 Conditional Probability 66\u003c\/p\u003e \u003cp\u003e4.6 Bayes' Theorem 68\u003c\/p\u003e \u003cp\u003e4.7 Assigning Probabilities 74\u003c\/p\u003e \u003cp\u003e4.8 Odds and Bayes Factor 75\u003c\/p\u003e \u003cp\u003e4.9 Beat the Dealer 76\u003c\/p\u003e \u003cp\u003eExercises 80\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Discrete Random Variables 83\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Discrete Random Variables 84\u003c\/p\u003e \u003cp\u003e5.2 Probability Distribution of a Discrete Random Variable 86\u003c\/p\u003e \u003cp\u003e5.3 Binomial Distribution 90\u003c\/p\u003e \u003cp\u003e5.4 Hypergeometric Distribution 92\u003c\/p\u003e \u003cp\u003e5.5 Poisson Distribution 93\u003c\/p\u003e \u003cp\u003e5.6 Joint Random Variables 96\u003c\/p\u003e \u003cp\u003e5.7 Conditional Probability for Joint Random Variables 100\u003c\/p\u003e \u003cp\u003eExercises 104\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Bayesian Inference for Discrete Random Variables 109\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Two Equivalent Ways of Using Bayes' Theorem 114\u003c\/p\u003e \u003cp\u003e6.2 Bayes' Theorem for Binomial with Discrete Prior 116\u003c\/p\u003e \u003cp\u003e6.3 Important Consequences of Bayes' Theorem 119\u003c\/p\u003e \u003cp\u003e6.4 Bayes' Theorem for Poisson with Discrete Prior 120\u003c\/p\u003e \u003cp\u003eExercises 122\u003c\/p\u003e \u003cp\u003eComputer Exercises 126\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Continuous Random Variables 129\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Probability Density Function 131\u003c\/p\u003e \u003cp\u003e7.2 Some Continuous Distributions 135\u003c\/p\u003e \u003cp\u003e7.3 Joint Continuous Random Variables 143\u003c\/p\u003e \u003cp\u003e7.4 Joint Continuous and Discrete Random Variables 144\u003c\/p\u003e \u003cp\u003eExercises 147\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Bayesian Inference for Binomial Proportion 149\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Using a Uniform Prior 150\u003c\/p\u003e \u003cp\u003e8.2 Using a Beta Prior 151\u003c\/p\u003e \u003cp\u003e8.3 Choosing Your Prior 154\u003c\/p\u003e \u003cp\u003e8.4 Summarizing the Posterior Distribution 158\u003c\/p\u003e \u003cp\u003e8.5 Estimating the Proportion 161\u003c\/p\u003e \u003cp\u003e8.6 Bayesian Credible Interval 162\u003c\/p\u003e \u003cp\u003eExercises 164\u003c\/p\u003e \u003cp\u003eComputer Exercises 167\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Comparing Bayesian and Frequentist Inferences for Proportion 169\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Frequentist Interpretation of Probability and Parameters 170\u003c\/p\u003e \u003cp\u003e9.2 Point Estimation 171\u003c\/p\u003e \u003cp\u003e9.3 Comparing Estimators for Proportion 174\u003c\/p\u003e \u003cp\u003e9.4 Interval Estimation 175\u003c\/p\u003e \u003cp\u003e9.5 Hypothesis Testing 178\u003c\/p\u003e \u003cp\u003e9.6 Testing a One-Sided Hypothesis 179\u003c\/p\u003e \u003cp\u003e9.7 Testing a Two-Sided Hypothesis 182\u003c\/p\u003e \u003cp\u003eExercises 187\u003c\/p\u003e \u003cp\u003eMonte Carlo Exercises 190\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Bayesian Inference for Poisson 193\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Some Prior Distributions for Poisson 194\u003c\/p\u003e \u003cp\u003e10.2 Inference for Poisson Parameter 200\u003c\/p\u003e \u003cp\u003eExercises 207\u003c\/p\u003e \u003cp\u003eComputer Exercises 208\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Bayesian Inference for Normal Mean 211\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Bayes' Theorem for Normal Mean with a Discrete Prior 211\u003c\/p\u003e \u003cp\u003e11.2 Bayes' Theorem for Normal Mean with a Continuous Prior 218\u003c\/p\u003e \u003cp\u003e11.3 Choosing Your Normal Prior 222\u003c\/p\u003e \u003cp\u003e11.4 Bayesian Credible Interval for Normal Mean 224\u003c\/p\u003e \u003cp\u003e11.5 Predictive Density for Next Observation 227\u003c\/p\u003e \u003cp\u003eExercises 230\u003c\/p\u003e \u003cp\u003eComputer Exercises 232\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Comparing Bayesian and Frequentist Inferences for Mean 237\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Comparing Frequentist and Bayesian Point Estimators 238\u003c\/p\u003e \u003cp\u003e12.2 Comparing Condence and Credible Intervals for Mean 241\u003c\/p\u003e \u003cp\u003e12.3 Testing a One-Sided Hypothesis about a Normal Mean 243\u003c\/p\u003e \u003cp\u003e12.4 Testing a Two-Sided Hypothesis about a Normal Mean 247\u003c\/p\u003e \u003cp\u003eExercises 251\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Bayesian Inference for Di erence Between Means 255\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Independent Random Samples from Two Normal Distributions 256\u003c\/p\u003e \u003cp\u003e13.2 Case 1: Equal Variances 257\u003c\/p\u003e \u003cp\u003e13.3 Case 2: Unequal Variances 262\u003c\/p\u003e \u003cp\u003e13.4 Bayesian Inference for Dierence Between Two Proportions Using Normal Approximation 265\u003c\/p\u003e \u003cp\u003e13.5 Normal Random Samples from Paired Experiments 266\u003c\/p\u003e \u003cp\u003eExercises 272\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Bayesian Inference for Simple Linear Regression 283\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Least Squares Regression 284\u003c\/p\u003e \u003cp\u003e14.2 Exponential Growth Model 288\u003c\/p\u003e \u003cp\u003e14.3 Simple Linear Regression Assumptions 290\u003c\/p\u003e \u003cp\u003e14.4 Bayes' Theorem for the Regression Model 292\u003c\/p\u003e \u003cp\u003e14.5 Predictive Distribution for Future Observation 298\u003c\/p\u003e \u003cp\u003eExercises 303\u003c\/p\u003e \u003cp\u003eComputer Exercises 312\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Bayesian Inference for Standard Deviation 315\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Bayes' Theorem for Normal Variance with a Continuous Prior 316\u003c\/p\u003e \u003cp\u003e15.2 Some Specic Prior Distributions and the Resulting Posteriors 318\u003c\/p\u003e \u003cp\u003e15.3 Bayesian Inference for Normal Standard Deviation 326\u003c\/p\u003e \u003cp\u003eExercises 332\u003c\/p\u003e \u003cp\u003eComputer Exercises 335\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Robust Bayesian Methods 337\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Eect of Misspecied Prior 338\u003c\/p\u003e \u003cp\u003e16.2 Bayes' Theorem with Mixture Priors 340\u003c\/p\u003e \u003cp\u003eExercises 349\u003c\/p\u003e \u003cp\u003eComputer Exercises 351\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Bayesian Inference for Normal with Unknown Mean and Variance 355\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 The Joint Likelihood Function 358\u003c\/p\u003e \u003cp\u003e17.2 Finding the Posterior when Independent Jeffreys' Priors for μ and σ2 Are Used 359\u003c\/p\u003e \u003cp\u003e17.3 Finding the Posterior when a Joint Conjugate Prior for μ and σ2 Is Used 361\u003c\/p\u003e \u003cp\u003e17.4 Difference Between Normal Means with Equal Unknown Variance 367\u003c\/p\u003e \u003cp\u003e17.5 Difference Between Normal Means with Unequal Unknown Variances 377\u003c\/p\u003e \u003cp\u003eComputer Exercises 383\u003c\/p\u003e \u003cp\u003eAppendix: Proof that the Exact Marginal Posterior Distribution of μ is \u003ci\u003eStudent's t\u003c\/i\u003e 385\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Bayesian Inference for Multivariate Normal Mean Vector 393\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e18.1 Bivariate Normal Density 394\u003c\/p\u003e \u003cp\u003e18.2 Multivariate Normal Distribution 397\u003c\/p\u003e \u003cp\u003e18.3 The Posterior Distribution of the Multivariate Normal Mean Vector when Covariance Matrix Is Known 398\u003c\/p\u003e \u003cp\u003e18.4 Credible Region for Multivariate Normal Mean Vector when Covariance Matrix Is Known 400\u003c\/p\u003e \u003cp\u003e18.5 Multivariate Normal Distribution with Unknown Covariance Matrix 402\u003c\/p\u003e \u003cp\u003eComputer Exercises 406\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Bayesian Inference for the Multiple Linear Regression Model 411\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e19.1 Least Squares Regression for Multiple Linear Regression Model 412\u003c\/p\u003e \u003cp\u003e19.2 Assumptions of Normal Multiple Linear Regression Model 414\u003c\/p\u003e \u003cp\u003e19.3 Bayes' Theorem for Normal Multiple Linear Regression Model 415\u003c\/p\u003e \u003cp\u003e19.4 Inference in the Multivariate Normal Linear Regression Model 419\u003c\/p\u003e \u003cp\u003e19.5 The Predictive Distribution for a Future Observation 425\u003c\/p\u003e \u003cp\u003eComputer Exercises 428\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Computational Bayesian Statistics Including Markov Chain Monte Carlo 431\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e20.1 Direct Methods for Sampling from the Posterior 436\u003c\/p\u003e \u003cp\u003e20.2 Sampling - Importance - Resampling 450\u003c\/p\u003e \u003cp\u003e20.3 Markov Chain Monte Carlo Methods 454\u003c\/p\u003e \u003cp\u003e20.4 Slice Sampling 470\u003c\/p\u003e \u003cp\u003e20.5 Inference from a Posterior Random Sample 473\u003c\/p\u003e \u003cp\u003e20.6 Where to Next? 475\u003c\/p\u003e \u003cp\u003eA Introduction to Calculus 477\u003c\/p\u003e \u003cp\u003eB Use of Statistical Tables 497\u003c\/p\u003e \u003cp\u003eC Using the Included Minitab Macros 523\u003c\/p\u003e \u003cp\u003eD Using the Included R Functions 543\u003c\/p\u003e \u003cp\u003eE Answers to Selected Exercises 565\u003c\/p\u003e \u003cp\u003eReferences 591\u003c\/p\u003e \u003cp\u003eIndex 595\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Mathematics [\u003ca title=\"See our other books on Mathematics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Mathematics%20%5BPB%5D%22\"\u003ePB\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley","offers":[{"title":"Brand New","offer_id":52165879431448,"sku":"9781118091562","price":89.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781118091562.jpg?v=1781101366","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/introduction-to-bayesian-statistics-hardback-9781118091562","provider":"Freshly Printed Books","version":"1.0","type":"link"}