In Chapter 7, the Binomial data model is based on the assumptions that a student’s chance of preferring dining out on Friday is the same for all students, and the dining preferences of different students are independent.
13.4.3 Disputed authorship of the Federalist PapersĬhapters 7, 8, and 9 make an underlying assumption about the source of data: observations are assumed to be identically and independently distributed (i.i.d.) following a single distribution with one or more unknown parameters. 13.4.2 A latent class model with two classes. 13.3.5 Estimating many trajectories by a hierarchical model. 13.3.2 Measuring hitting performance in baseball. 13.2.6 Which words distinguish the two authors?. 13.2.5 Comparison of rates for two authors. 12.2 Bayesian Multiple Linear Regression. 12 Bayesian Multiple Regression and Logistic Models. 11.7 Bayesian Inferences with Simple Linear Regression. 11.2 Example: Prices and Areas of House Sales. 10.3.2 A hierarchical Beta-Binomial model. 10.3.1 Example: Deaths after heart attack. 10.3 Hierarchical Beta-Binomial Modeling. 10.2.2 A hierarchical Normal model with random \(\sigma\). 10.2.1 Example: ratings of animation movies. 10.1.5 A two-stage prior leading to compromise estimates. 10.1.2 Example: standardized test scores. 9.6.1 Burn-in, starting values, and multiple chains. 9.5.3 Normal sampling – both parameters unknown. 9.4.1 Choice of starting value and proposal region. 9.3.3 A general function for the Metropolis algorithm. 9.3.1 Example: Walking on a number line. 
9 Simulation by Markov Chain Monte Carlo. 8.8.4 Case study: Learning about website counts. 8.6.1 Bayesian hypothesis testing and credible interval. 8.6 Bayesian Inferences for Continuous Normal Mean. Smoothing parameter jmp 9 graph builder update#
8.5.2 A quick peak at the update procedure.8.4.1 The Normal prior for mean \(\mu\).8.3.3 Inference: Federer’s time-to-serve.8.3.1 Example: Roger Federer’s time-to-serve.8.3 Bayesian Inference with Discrete Priors.
7.5 Bayesian Inferences with Continuous Priors.7.4.2 From Beta prior to Beta posterior.7.3.1 The Beta distribution and probabilities.7.2.6 Discussion: using a discrete prior.
7.2.5 Inference: students’ dining preference. 7.2.4 Posterior distribution for proportion \(p\). 7.2.2 Discrete prior distributions for proportion \(p\). 7.2.1 Example: students’ dining preference. 7.2 Bayesian Inference with Discrete Priors. 7.1 Introduction: Thinking About a Proportion Subjectively. 7 Learning About a Binomial Probability. 6.6 Flipping a Random Coin: The Beta-Binomial Distribution. 6.5 Independence and Measuring Association. 6.2 Joint Probability Mass Function: Sampling From a Box. 5.3 Binomial Probabilities and the Normal Curve. 5.1 Introduction: A Baseball Spinner Game. 4.5.3 Mean and standard deviation of a Binomial. 4.4 Standard Deviation of a Probability Distribution. 4.3 Summarizing a Probability Distribution. 4.2 Random Variable and Probability Distribution. 4.1 Introduction: The Hat Check Problem. 
3.7 R Example: Learning About a Spinner. 3.5 The Multiplication Rule Under Independence. 3.4 Definition and the Multiplication Rule. 3.1 Introduction: The Three Card Problem. 2.6 Arrangements of Non-Distinct Objects. 2.1 Introduction: Rolling Dice, Yahtzee, and Roulette. 1.9 The Complement and Addition Properties. 1.4 The Subjective View of a Probability. 1.3 The Frequency View of a Probability. 1.2 The Classical View of a Probability. 1 Probability: A Measurement of Uncertainty.
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