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# thinkbayes

By Unknown
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Book Description
• Preface
• My theory, which is mine
• Modeling and approximation
• Working with the code
• Code style
• Prerequisites
• Bayes's Theorem
• Conditional probability
• Conjoint probability
• Bayes's theorem
• The diachronic interpretation
• The M&M problem
• The Monty Hall problem
• Discussion
• Computational Statistics
• Distributions
• The Bayesian framework
• The Monty Hall problem
• Encapsulating the framework
• The M&M problem
• Discussion
• Exercises
• Estimation
• The dice problem
• The locomotive problem
• An alternative prior
• Credible intervals
• Cumulative distribution functions
• The German tank problem
• Discussion
• Exercises
• More Estimation
• The Euro problem
• Summarizing the posterior
• Swamping the priors
• Optimization
• The beta distribution
• Discussion
• Exercises
• Odds
• The odds form of Bayes's theorem
• Oliver's blood
• Maxima
• Mixtures
• Discussion
• Decision Analysis
• The Price is Right problem
• The prior
• Probability density functions
• Representing PDFs
• Modeling the contestants
• Likelihood
• Update
• Optimal bidding
• Discussion
• Prediction
• The Boston Bruins problem
• Poisson processes
• The posteriors
• The distribution of goals
• The probability of winning
• Sudden death
• Discussion
• Exercises
• Observer Bias
• The Red Line problem
• The model
• Wait times
• Predicting wait times
• Estimating the arrival rate
• Incorporating uncertainty
• Decision analysis
• Discussion
• Exercises
• Two Dimensions
• Paintball
• The suite
• Trigonometry
• Likelihood
• Joint distributions
• Conditional distributions
• Credible intervals
• Discussion
• Exercises
• Approximate Bayesian Computation
• The Variability Hypothesis
• Mean and standard deviation
• Update
• The posterior distribution of CV
• Underflow
• Log-likelihood
• A little optimization
• ABC
• Robust estimation
• Who is more variable?
• Discussion
• Exercises
• Hypothesis Testing
• Back to the Euro problem
• Making a fair comparison
• The triangle prior
• Discussion
• Exercises
• Evidence
• Interpreting SAT scores
• The scale
• The prior
• Posterior
• A better model
• Calibration
• Posterior distribution of efficacy
• Predictive distribution
• Discussion
• Simulation
• The Kidney Tumor problem
• A simple model
• A more general model
• Implementation
• Caching the joint distribution
• Conditional distributions
• Serial Correlation
• Discussion
• A Hierarchical Model
• The Geiger counter problem
• Start simple
• Make it hierarchical
• A little optimization
• Extracting the posteriors
• Discussion
• Exercises
• Dealing with Dimensions
• Belly button bacteria
• Lions and tigers and bears
• The hierarchical version
• Random sampling
• Optimization
• Collapsing the hierarchy
• One more problem
• We're not done yet
• The belly button data
• Predictive distributions
• Joint posterior
• Coverage
• Discussion
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