Table of Contents

Proofs are printed in bold – Definitions are set in italics
Proofs: by Number, by Topic – Definitions: by Number, by Topic
Specials: General Theorems, Probability Distributions, Statistical Models, Model Selection Criteria

Chapter I: General Theorems

1. Probability theory

   1.1. Random experiments
      1.1.1. Random experiment
      1.1.2. Sample space
      1.1.3. Event space
      1.1.4. Probability space
      1.1.5. Measured data
      1.1.6. Statistical sample
      1.1.7. Sample statistic
   

   1.2. Random variables
      1.2.1. Random event
      1.2.2. Random variable
      1.2.3. Random vector
      1.2.4. Random matrix
      1.2.5. Constant
      1.2.6. Discrete vs. continuous
      1.2.7. Univariate vs. multivariate
      1.2.8. independent and identically distributed
   

   1.3. Probability
      1.3.1. Probability
      1.3.2. Joint probability
      1.3.3. Marginal probability
      1.3.4. Conditional probability
      1.3.5. Exceedance probability
      1.3.6. Statistical independence
      1.3.7. Conditional independence
      1.3.8. Self-independence
      1.3.9. Probability under independence
      1.3.10. Mutual exclusivity
      1.3.11. Probability under exclusivity
   

   1.4. Probability axioms
      1.4.1. Axioms of probability
      1.4.2. Monotonicity of probability (1)
      1.4.3. Monotonicity of probability (2)
      1.4.4. Probability of the empty set (1)
      1.4.5. Probability of the empty set (2)
      1.4.6. Probability of the complement
      1.4.7. Range of probability
      1.4.8. Addition law of probability
      1.4.9. Law of total probability
      1.4.10. Probability of exhaustive events (1)
      1.4.11. Probability of exhaustive events (2)
   

   1.5. Probability distributions
      1.5.1. Probability distribution
      1.5.2. Joint distribution
      1.5.3. Marginal distribution
      1.5.4. Conditional distribution
      1.5.5. Unimodal vs. multimodal distribution
      1.5.6. Sampling distribution
      1.5.7. Statistical parameter
      1.5.8. Degrees of freedom
   

   1.6. Probability mass function
      1.6.1. Definition
      1.6.2. Probability mass function of sum of independents
      1.6.3. Probability mass function of strictly increasing function
      1.6.4. Probability mass function of strictly decreasing function
      1.6.5. Probability mass function of invertible function
   

   1.7. Probability density function
      1.7.1. Definition
      1.7.2. Probability density function of sum of independents
      1.7.3. Probability density function of strictly increasing function
      1.7.4. Probability density function of strictly decreasing function
      1.7.5. Probability density function of invertible function
      1.7.6. Probability density function of linear transformation
      1.7.7. Probability density function in terms of cumulative distribution function
   

   1.8. Cumulative distribution function
      1.8.1. Definition
      1.8.2. Cumulative distribution function of sum of independents
      1.8.3. Cumulative distribution function of strictly increasing function
      1.8.4. Cumulative distribution function of strictly decreasing function
      1.8.5. Cumulative distribution function of discrete random variable
      1.8.6. Cumulative distribution function of continuous random variable
      1.8.7. Exceedance probability based on cumulative distribution function
      1.8.7. Probability integral transform
      1.8.8. Inverse transformation method
      1.8.9. Distributional transformation
      1.8.10. Joint cumulative distribution function
   

   1.9. Other probability functions
      1.9.1. Quantile function
      1.9.2. Quantile function in terms of cumulative distribution function
      1.9.3. Characteristic function
      1.9.4. Characteristic function of arbitrary function
      1.9.5. Moment-generating function
      1.9.6. Moment-generating function of sum of independents
      1.9.7. Moment-generating function of arbitrary function
      1.9.8. Moment-generating function of linear transformation
      1.9.9. Moment-generating function of linear combination
      1.9.10. Probability-generating function
      1.9.11. Probability-generating function in terms of expected value
      1.9.12. Probability-generating function of zero
      1.9.13. Probability-generating function of one
      1.9.14. Cumulant-generating function
   

   1.10. Expected value
      1.10.1. Definition
      1.10.2. Sample mean
      1.10.3. Non-negative random variable
      1.10.4. Non-negativity
      1.10.5. Linearity
      1.10.6. Monotonicity
      1.10.7. (Non-)Multiplicativity
      1.10.8. Expectation of a trace
      1.10.9. Expectation of a quadratic form
      1.10.10. Squared expectation of a product
      1.10.11. Expected value minimizes squared error
      1.10.12. Law of total expectation
      1.10.13. Law of the unconscious statistician
      1.10.14. Weak law of large numbers
      1.10.15. Expected value of a random vector
      1.10.16. Expected value of a random matrix
   

   1.11. Variance
      1.11.1. Definition
      1.11.2. Sample variance
      1.11.3. Partition into expected values
      1.11.4. Non-negativity
      1.11.5. Variance of a constant
      1.11.6. Invariance under addition
      1.11.7. Scaling upon multiplication
      1.11.8. Variance of a sum
      1.11.9. Variance of linear combination
      1.11.10. Additivity under independence
      1.11.11. Law of total variance
      1.11.12. Precision
   

   1.12. Skewness
      1.12.1. Definition
      1.12.2. Sample skewness
      1.12.3. Partition into expected values
   

   1.13. Covariance
      1.13.1. Definition
      1.13.2. Sample covariance
      1.13.3. Partition into expected values
      1.13.4. Symmetry
      1.13.5. Self-covariance
      1.13.6. Covariance under independence
      1.13.7. Relationship to correlation
      1.13.8. Law of total covariance
      1.13.9. Covariance matrix
      1.13.10. Sample covariance matrix
      1.13.11. Covariance matrix and expected values
      1.13.12. Symmetry
      1.13.13. Positive semi-definiteness
      1.13.14. Invariance under addition of vector
      1.13.15. Scaling upon multiplication with matrix
      1.13.16. Cross-covariance matrix
      1.13.17. Covariance matrix of a sum
      1.13.18. Covariance matrix and correlation matrix
      1.13.19. Precision matrix
      1.13.20. Precision matrix and correlation matrix
   

   1.14. Correlation
      1.14.1. Definition
      1.14.2. Range
      1.14.3. Correlation under independence
      1.14.4. Sample correlation coefficient
      1.14.5. Relationship to standard scores
      1.14.6. Correlation matrix
      1.14.7. Sample correlation matrix
   

   1.15. Measures of central tendency
      1.15.1. Median
      1.15.2. Median minimizes mean absolute error
      1.15.3. Mode
   

   1.16. Measures of statistical dispersion
      1.16.1. Standard deviation
      1.16.2. Full width at half maximum
   

   1.17. Further summary statistics
      1.17.1. Minimum
      1.17.2. Maximum
   

   1.18. Further moments
      1.18.1. Moment
      1.18.2. Moment in terms of moment-generating function
      1.18.3. Raw moment
      1.18.4. First raw moment is mean
      1.18.5. Second raw moment and variance
      1.18.6. Central moment
      1.18.7. First central moment is zero
      1.18.8. Second central moment is variance
      1.18.9. Standardized moment
   

2. Information theory

   2.1. Shannon entropy
      2.1.1. Definition
      2.1.2. Non-negativity
      2.1.3. Concavity
      2.1.4. Conditional entropy
      2.1.5. Joint entropy
      2.1.6. Cross-entropy
      2.1.7. Convexity of cross-entropy
      2.1.8. Gibbs’ inequality
      2.1.9. Log sum inequality
   

   2.2. Differential entropy
      2.2.1. Definition
      2.2.2. Negativity
      2.2.3. Invariance under addition
      2.2.4. Addition upon multiplication
      2.2.5. Addition upon matrix multiplication
      2.2.6. Non-invariance and transformation
      2.2.7. Conditional differential entropy
      2.2.8. Joint differential entropy
      2.2.9. Differential cross-entropy
   

   2.3. Discrete mutual information
      2.3.1. Definition
      2.3.2. Relation to marginal and conditional entropy
      2.3.3. Relation to marginal and joint entropy
      2.3.4. Relation to joint and conditional entropy
   

   2.4. Continuous mutual information
      2.4.1. Definition
      2.4.2. Relation to marginal and conditional differential entropy
      2.4.3. Relation to marginal and joint differential entropy
      2.4.4. Relation to joint and conditional differential entropy
   

   2.5. Kullback-Leibler divergence
      2.5.1. Definition
      2.5.2. Non-negativity (1)
      2.5.3. Non-negativity (2)
      2.5.4. Non-symmetry
      2.5.5. Convexity
      2.5.6. Additivity for independent distributions
      2.5.7. Invariance under parameter transformation
      2.5.8. Relation to discrete entropy
      2.5.9. Relation to differential entropy
   

3. Estimation theory

   3.1. Basic concepts of estimation
      3.1.1. Estimator
      3.1.2. Biased vs. unbiased
   

   3.2. Point estimates
      3.2.1. Mean squared error
      3.2.2. Partition of the mean squared error into bias and variance
   

   3.3. Interval estimates
      3.3.1. Confidence interval
      3.3.2. Construction of confidence intervals using Wilks’ theorem
   

4. Frequentist statistics

   4.1. Likelihood theory
      4.1.1. Likelihood function
      4.1.2. Log-likelihood function
      4.1.3. Maximum likelihood estimation
      4.1.4. Maximum log-likelihood
      4.1.5. MLE can be biased
      4.1.6. Likelihood ratio
      4.1.7. Log-likelihood ratio
      4.1.8. Method of moments
   

   4.2. Statistical hypotheses
      4.2.1. Statistical hypothesis
      4.2.2. Simple vs. composite
      4.2.3. Point/exact vs. set/inexact
      4.2.4. One-tailed vs. two-tailed
   

   4.3. Hypothesis testing
      4.3.1. Statistical test
      4.3.2. Null hypothesis
      4.3.3. Alternative hypothesis
      4.3.4. One-tailed vs. two-tailed
      4.3.5. Test statistic
      4.3.6. Size of a test
      4.3.7. Power of a test
      4.3.8. Significance level
      4.3.9. Critical value
      4.3.10. p-value
      4.3.11. Distribution of p-value under null hypothesis
   

5. Bayesian statistics

   5.1. Probabilistic modeling
      5.1.1. Generative model
      5.1.2. Likelihood function
      5.1.3. Prior distribution
      5.1.4. Prior predictive distribution
      5.1.5. Full probability model
      5.1.6. Joint likelihood
      5.1.7. Joint likelihood is product of likelihood and prior
      5.1.8. Posterior distribution
      5.1.9. Posterior predictive distribution
      5.1.10. Posterior density is proportional to joint likelihood
      5.1.11. Combined posterior distribution from independent data
      5.1.12. Posterior predictive distribution is marginal of joint likelihood
      5.1.13. Maximum-a-posteriori estimation
      5.1.14. Marginal likelihood
      5.1.15. Marginal likelihood is integral of joint likelihood
   

   5.2. Prior distributions
      5.2.1. Flat vs. hard vs. soft
      5.2.2. Uniform vs. non-uniform
      5.2.3. Informative vs. non-informative
      5.2.4. Empirical vs. non-empirical
      5.2.5. Conjugate vs. non-conjugate
      5.2.6. Maximum entropy priors
      5.2.7. Empirical Bayes priors
      5.2.8. Reference priors
   

   5.3. Bayesian inference
      5.3.1. Bayes’ theorem
      5.3.2. Bayes’ rule
      5.3.3. Empirical Bayes
      5.3.4. Variational Bayes
   

6. Machine learning

   6.1. Scoring rules
      6.1.1. Scoring rule
      6.1.2. Proper scoring rule
      6.1.3. Strictly proper scoring rule
      6.1.4. Log probability scoring rule
      6.1.5. Log probability is strictly proper scoring rule
      6.1.6. Brier scoring rule
      6.1.7. Brier scoring rule is strictly proper scoring rule
   

Chapter II: Probability Distributions

1. Univariate discrete distributions

   1.1. Discrete uniform distribution
      1.1.1. Definition
      1.1.2. Probability mass function
      1.1.3. Cumulative distribution function
      1.1.4. Quantile function
      1.1.5. Shannon entropy
      1.1.6. Kullback-Leibler divergence
      1.1.7. Maximum entropy distribution
   

   1.2. Bernoulli distribution
      1.2.1. Definition
      1.2.2. Probability mass function
      1.2.3. Mean
      1.2.4. Variance
      1.2.5. Range of variance
      1.2.6. Shannon entropy
      1.2.7. Kullback-Leibler divergence
   

   1.3. Binomial distribution
      1.3.1. Definition
      1.3.2. Probability mass function
      1.3.3. Probability-generating function
      1.3.4. Mean
      1.3.5. Variance
      1.3.6. Range of variance
      1.3.7. Shannon entropy
      1.3.8. Kullback-Leibler divergence
      1.3.9. Conditional binomial
   

   1.4. Beta-binomial distribution
      1.4.1. Definition
      1.4.2. Probability mass function
      1.4.3. Probability mass function in terms of gamma function
      1.4.4. Cumulative distribution function
   

   1.5. Poisson distribution
      1.5.1. Definition
      1.5.2. Probability mass function
      1.5.3. Mean
      1.5.4. Variance
   

2. Multivariate discrete distributions

   2.1. Categorical distribution
      2.1.1. Definition
      2.1.2. Probability mass function
      2.1.3. Mean
      2.1.4. Covariance
      2.1.5. Shannon entropy
   

   2.2. Multinomial distribution
      2.2.1. Definition
      2.2.2. Probability mass function
      2.2.3. Mean
      2.2.4. Covariance
      2.2.5. Shannon entropy
   

3. Univariate continuous distributions

   3.1. Continuous uniform distribution
      3.1.1. Definition
      3.1.2. Standard uniform distribution
      3.1.3. Probability density function
      3.1.4. Cumulative distribution function
      3.1.5. Quantile function
      3.1.6. Mean
      3.1.7. Median
      3.1.8. Mode
      3.1.9. Variance
      3.1.10. Differential entropy
      3.1.11. Kullback-Leibler divergence
      3.1.12. Maximum entropy distribution
   

   3.2. Normal distribution
      3.2.1. Definition
      3.2.2. Special case of multivariate normal distribution
      3.2.3. Standard normal distribution
      3.2.4. Relationship to standard normal distribution (1)
      3.2.5. Relationship to standard normal distribution (2)
      3.2.6. Relationship to standard normal distribution (3)
      3.2.7. Relationship to chi-squared distribution
      3.2.8. Relationship to t-distribution
      3.2.9. Gaussian integral
      3.2.10. Probability density function
      3.2.11. Moment-generating function
      3.2.12. Cumulative distribution function
      3.2.13. Cumulative distribution function without error function
      3.2.14. Probability of being within standard deviations from mean
      3.2.15. Quantile function
      3.2.16. Mean
      3.2.17. Median
      3.2.18. Mode
      3.2.19. Variance
      3.2.20. Full width at half maximum
      3.2.21. Extreme points
      3.2.22. Inflection points
      3.2.23. Differential entropy
      3.2.24. Kullback-Leibler divergence
      3.2.25. Maximum entropy distribution
      3.2.26. Linear combination of independent normals
      3.2.27. Normal and uncorrelated does not imply independent
      3.2.28. Marginally normal does not imply jointly normal
   

   3.3. t-distribution
      3.3.1. Definition
      3.3.2. Special case of multivariate t-distribution
      3.3.3. Non-standardized t-distribution
      3.3.4. Relationship to non-standardized t-distribution
      3.3.5. Probability density function
   

   3.4. Gamma distribution
      3.4.1. Definition
      3.4.2. Special case of Wishart distribution
      3.4.3. Standard gamma distribution
      3.4.4. Relationship to standard gamma distribution (1)
      3.4.5. Relationship to standard gamma distribution (2)
      3.4.6. Scaling of a gamma random variable
      3.4.7. Probability density function
      3.4.8. Moment-generating function
      3.4.9. Cumulative distribution function
      3.4.10. Quantile function
      3.4.11. Mean
      3.4.12. Variance
      3.4.13. Logarithmic expectation
      3.4.14. Expectation of x ln x
      3.4.15. Differential entropy
      3.4.16. Kullback-Leibler divergence
   

   3.5. Exponential distribution
      3.5.1. Definition
      3.5.2. Special case of gamma distribution
      3.5.3. Probability density function
      3.5.4. Moment-generating function
      3.5.5. Cumulative distribution function
      3.5.6. Quantile function
      3.5.7. Mean
      3.5.8. Median
      3.5.9. Mode
      3.5.10. Variance
      3.5.11. Skewness
   

   3.6. Log-normal distribution
      3.6.1. Definition
      3.6.2. Probability density function
      3.6.3. Cumulative distribution function
      3.6.4. Quantile function
      3.6.5. Mean
      3.6.6. Median
      3.6.7. Mode
      3.6.8. Variance
      3.6.9. Product of independent log-normals
   

   3.7. Chi-squared distribution
      3.7.1. Definition
      3.7.2. Special case of gamma distribution
      3.7.3. Probability density function
      3.7.4. Moments
   

   3.8. F-distribution
      3.8.1. Definition
      3.8.2. Probability density function
   

   3.9. Beta distribution
      3.9.1. Definition
      3.9.2. Relationship to chi-squared distribution
      3.9.3. Probability density function
      3.9.4. Moment-generating function
      3.9.5. Cumulative distribution function
      3.9.6. Mean
      3.9.7. Variance
   

   3.10. Wald distribution
      3.10.1. Definition
      3.10.2. Probability density function
      3.10.3. Moment-generating function
      3.10.4. Mean
      3.10.5. Variance
      3.10.6. Skewness
      3.10.7. Method of moments
   

   3.11. ex-Gaussian distribution
      3.11.1. Definition
      3.11.2. Probability density function
      3.11.3. Moment-generating function
      3.11.4. Mean
      3.11.5. Variance
      3.11.6. Skewness
      3.11.7. Method of moments
   

4. Multivariate continuous distributions

   4.1. Multivariate normal distribution
      4.1.1. Definition
      4.1.2. Special case of matrix-normal distribution
      4.1.3. Relationship to chi-squared distribution
      4.1.4. Probability density function
      4.1.5. Moment-generating function
      4.1.6. Mean
      4.1.7. Covariance
      4.1.8. Differential entropy
      4.1.9. Mutual information
      4.1.10. Kullback-Leibler divergence
      4.1.11. Linear transformation
      4.1.12. Marginal distributions
      4.1.13. Conditional distributions
      4.1.14. Conditions for independence
      4.1.15. Independence of products
   

   4.2. Bivariate normal distribution
      4.2.1. Definition
      4.2.2. Probability density function
      4.2.3. Probability density function in terms of correlation coefficient
      4.2.4. Mutual information
      4.2.5. Linear combination
   

   4.3. Multivariate t-distribution
      4.3.1. Definition
      4.3.2. Probability density function
      4.3.3. Relationship to F-distribution
   

   4.4. Normal-gamma distribution
      4.4.1. Definition
      4.4.2. Special case of normal-Wishart distribution
      4.4.3. Probability density function
      4.4.4. Mean
      4.4.5. Covariance
      4.4.6. Differential entropy
      4.4.7. Kullback-Leibler divergence
      4.4.8. Marginal distributions
      4.4.9. Conditional distributions
      4.4.10. Drawing samples
   

   4.5. Dirichlet distribution
      4.5.1. Definition
      4.5.2. Probability density function
      4.5.3. Kullback-Leibler divergence
      4.5.4. Exceedance probabilities
   

5. Matrix-variate continuous distributions

   5.1. Matrix-normal distribution
      5.1.1. Definition
      5.1.2. Equivalence to multivariate normal distribution
      5.1.3. Probability density function
      5.1.4. Mean
      5.1.5. Covariance
      5.1.6. Differential entropy
      5.1.7. Kullback-Leibler divergence
      5.1.8. Transposition
      5.1.9. Linear transformation
      5.1.10. Marginal distributions
      5.1.11. Drawing samples
   

   5.2. Wishart distribution
      5.2.1. Definition
      5.2.2. Kullback-Leibler divergence
   

   5.3. Normal-Wishart distribution
      5.3.1. Definition
      5.3.2. Probability density function
      5.3.3. Mean
   

Chapter III: Statistical Models

1. Univariate normal data

   1.1. Univariate Gaussian
      1.1.1. Definition
      1.1.2. Maximum likelihood estimation
      1.1.3. One-sample t-test
      1.1.4. Two-sample t-test
      1.1.5. Paired t-test
      1.1.6. F-test for equality of variances
      1.1.7. Conjugate prior distribution
      1.1.8. Posterior distribution
      1.1.9. Log model evidence
      1.1.10. Accuracy and complexity
   

   1.2. Univariate Gaussian with known variance
      1.2.1. Definition
      1.2.2. Maximum likelihood estimation
      1.2.3. One-sample z-test
      1.2.4. Two-sample z-test
      1.2.5. Paired z-test
      1.2.6. Conjugate prior distribution
      1.2.7. Posterior distribution
      1.2.8. Log model evidence
      1.2.9. Accuracy and complexity
      1.2.10. Log Bayes factor
      1.2.11. Expectation of log Bayes factor
      1.2.12. Cross-validated log model evidence
      1.2.13. Cross-validated log Bayes factor
      1.2.14. Expectation of cross-validated log Bayes factor
   

   1.3. Analysis of variance
      1.3.1. One-way ANOVA
      1.3.2. Treatment sum of squares
      1.3.3. Ordinary least squares for one-way ANOVA
      1.3.4. Sums of squares in one-way ANOVA
      1.3.5. F-test for main effect in one-way ANOVA
      1.3.6. F-statistic in terms of OLS estimates
      1.3.7. Reparametrization of one-way ANOVA
      1.3.8. Two-way ANOVA
      1.3.9. Interaction sum of squares
      1.3.10. Ordinary least squares for two-way ANOVA
      1.3.11. Sums of squares in two-way ANOVA
      1.3.12. Cochran’s theorem for two-way ANOVA
      1.3.13. F-test for main effect in two-way ANOVA
      1.3.14. F-test for interaction in two-way ANOVA
      1.3.15. F-test for grand mean in two-way ANOVA
      1.3.16. F-statistics in terms of OLS estimates
   

   1.4. Simple linear regression
      1.4.1. Definition
      1.4.2. Special case of multiple linear regression
      1.4.3. Ordinary least squares (1)
      1.4.4. Ordinary least squares (2)
      1.4.5. Expectation of estimates
      1.4.6. Variance of estimates
      1.4.7. Distribution of estimates
      1.4.8. Correlation of estimates
      1.4.9. Effects of mean-centering
      1.4.10. Regression line
      1.4.11. Regression line includes center of mass
      1.4.12. Projection of data point to regression line
      1.4.13. Sums of squares
      1.4.14. Partition of sums of squares
      1.4.15. Transformation matrices
      1.4.16. Weighted least squares (1)
      1.4.17. Weighted least squares (2)
      1.4.18. Maximum likelihood estimation (1)
      1.4.19. Maximum likelihood estimation (2)
      1.4.20. t-test for intercept parameter
      1.4.21. t-test for slope parameter
      1.4.22. F-test for model comparison
      1.4.23. Sum of residuals is zero
      1.4.24. Correlation with covariate is zero
      1.4.25. Residual variance in terms of sample variance
      1.4.26. Correlation coefficient in terms of slope estimate
      1.4.27. Coefficient of determination in terms of correlation coefficient
   

   1.5. Multiple linear regression
      1.5.1. Definition
      1.5.2. Special case of general linear model
      1.5.3. Ordinary least squares (1)
      1.5.4. Ordinary least squares (2)
      1.5.5. Ordinary least squares (3)
      1.5.6. Ordinary least squares for two regressors
      1.5.7. Total sum of squares
      1.5.8. Explained sum of squares
      1.5.9. Residual sum of squares
      1.5.10. Total, explained and residual sum of squares
      1.5.11. Estimation matrix
      1.5.12. Projection matrix
      1.5.13. Residual-forming matrix
      1.5.14. Estimation, projection and residual-forming matrix
      1.5.15. Symmetry of projection and residual-forming matrix
      1.5.16. Idempotence of projection and residual-forming matrix
      1.5.17. Independence of estimated parameters and residuals
      1.5.18. Distribution of OLS estimates, signal and residuals
      1.5.19. Distribution of WLS estimates, signal and residuals
      1.5.20. Distribution of residual sum of squares
      1.5.21. Weighted least squares (1)
      1.5.22. Weighted least squares (2)
      1.5.23. Maximum likelihood estimation
      1.5.24. Maximum log-likelihood
      1.5.25. Log-likelihood ratio
      1.5.26. t-contrast
      1.5.27. F-contrast
      1.5.28. Contrast-based t-test
      1.5.29. Contrast-based F-test
      1.5.30. t-test for single regressor
      1.5.31. F-test for multiple regressors
      1.5.32. Deviance function
      1.5.33. Akaike information criterion
      1.5.34. Bayesian information criterion
      1.5.35. Corrected Akaike information criterion
   

   1.6. Bayesian linear regression
      1.6.1. Conjugate prior distribution
      1.6.2. Posterior distribution
      1.6.3. Log model evidence
      1.6.4. Accuracy and complexity
      1.6.5. Deviance information criterion
      1.6.6. Maximum-a-posteriori estimation
      1.6.7. Expression of posterior parameters using error terms
      1.6.8. Posterior probability of alternative hypothesis
      1.6.9. Posterior credibility region excluding null hypothesis
      1.6.10. Combined posterior distribution from independent data sets
      1.6.11. Log Bayes factor for comparison of two regression models
   

   1.7. Bayesian linear regression with known covariance
      1.7.1. Conjugate prior distribution
      1.7.2. Posterior distribution
      1.7.3. Log model evidence
      1.7.4. Accuracy and complexity
   

2. Multivariate normal data

   2.1. General linear model
      2.1.1. Definition
      2.1.2. Ordinary least squares
      2.1.3. Weighted least squares
      2.1.4. Maximum likelihood estimation
      2.1.5. Maximum log-likelihood
      2.1.6. Log-likelihood ratio
      2.1.7. Mutual information
      2.1.8. Log-likelihood ratio and estimated mutual information
   

   2.2. Transformed general linear model
      2.2.1. Definition
      2.2.2. Derivation of the distribution
      2.2.3. Equivalence of parameter estimates
   

   2.3. Inverse general linear model
      2.3.1. Definition
      2.3.2. Derivation of the distribution
      2.3.3. Best linear unbiased estimator
      2.3.4. Equivalence of log-likelihood ratios
      2.3.5. Corresponding forward model
      2.3.6. Derivation of parameters
      2.3.7. Proof of existence
   

   2.4. Multivariate Bayesian linear regression
      2.4.1. Conjugate prior distribution
      2.4.2. Posterior distribution
      2.4.3. Log model evidence
   

3. Count data

   3.1. Binomial observations
      3.1.1. Definition
      3.1.2. Binomial test
      3.1.3. Maximum likelihood estimation
      3.1.4. Maximum log-likelihood
      3.1.5. Maximum-a-posteriori estimation
      3.1.6. Conjugate prior distribution
      3.1.7. Posterior distribution
      3.1.8. Log model evidence
      3.1.9. Log Bayes factor
      3.1.10. Posterior probability
   

   3.2. Multinomial observations
      3.2.1. Definition
      3.2.2. Multinomial test
      3.2.3. Maximum likelihood estimation
      3.2.4. Maximum log-likelihood
      3.2.5. Maximum-a-posteriori estimation
      3.2.6. Conjugate prior distribution
      3.2.7. Posterior distribution
      3.2.8. Log model evidence
      3.2.9. Log Bayes factor
      3.2.10. Posterior probability
   

   3.3. Poisson-distributed data
      3.3.1. Definition
      3.3.2. Maximum likelihood estimation
      3.3.3. Conjugate prior distribution
      3.3.4. Posterior distribution
      3.3.5. Log model evidence
   

   3.4. Poisson distribution with exposure values
      3.4.1. Definition
      3.4.2. Maximum likelihood estimation
      3.4.3. Conjugate prior distribution
      3.4.4. Posterior distribution
      3.4.5. Log model evidence
   

4. Frequency data

   4.1. Beta-distributed data
      4.1.1. Definition
      4.1.2. Method of moments
   

   4.2. Dirichlet-distributed data
      4.2.1. Definition
      4.2.2. Maximum likelihood estimation
   

   4.3. Beta-binomial data
      4.3.1. Definition
      4.3.2. Method of moments
   

5. Categorical data

   5.1. Logistic regression
      5.1.1. Definition
      5.1.2. Probability and log-odds
      5.1.3. Log-odds and probability
   

Chapter IV: Model Selection

1. Goodness-of-fit measures

   1.1. Residual variance
      1.1.1. Definition
      1.1.2. Maximum likelihood estimator is biased (p = 1)
      1.1.3. Maximum likelihood estimator is biased (p > 1)
      1.1.4. Construction of unbiased estimator (p = 1)
      1.1.5. Construction of unbiased estimator (p > 1)
   

   1.2. R-squared
      1.2.1. Definition
      1.2.2. Derivation of R² and adjusted R²
      1.2.3. Relationship to residual variance
      1.2.4. Relationship to maximum log-likelihood
      1.2.5. Statistical significance test for R²
   

   1.3. F-statistic
      1.3.1. Definition
      1.3.2. Relationship to coefficient of determination
      1.3.3. Relationship to maximum log-likelihood
   

   1.4. Signal-to-noise ratio
      1.4.1. Definition
      1.4.2. Relationship to coefficient of determination
      1.4.3. Relationship to maximum log-likelihood
   

2. Classical information criteria

   2.1. Akaike information criterion
      2.1.1. Definition
      2.1.2. Corrected AIC
      2.1.3. Corrected AIC and uncorrected AIC
      2.1.4. Corrected AIC and maximum log-likelihood
   

   2.2. Bayesian information criterion
      2.2.1. Definition
      2.2.2. Derivation
   

   2.3. Deviance information criterion
      2.3.1. Definition
      2.3.2. Deviance
   

3. Bayesian model selection

   3.1. Model evidence
      3.1.1. Definition
      3.1.2. Derivation
      3.1.3. Log model evidence
      3.1.4. Derivation of the log model evidence
      3.1.5. Expression using prior and posterior
      3.1.6. Partition into accuracy and complexity
      3.1.7. Subtraction of mean from LMEs
      3.1.8. Uniform-prior log model evidence
      3.1.9. Cross-validated log model evidence
      3.1.10. Empirical Bayesian log model evidence
      3.1.11. Variational Bayesian log model evidence
   

   3.2. Family evidence
      3.2.1. Definition
      3.2.2. Derivation
      3.2.3. Log family evidence
      3.2.4. Derivation of the log family evidence
      3.2.5. Calculation from log model evidences
      3.2.6. Approximation of log family evidences
   

   3.3. Bayes factor
      3.3.1. Definition
      3.3.2. Transitivity
      3.3.3. Computation using Savage-Dickey density ratio
      3.3.4. Computation using encompassing prior method
      3.3.5. Encompassing model
      3.3.6. Log Bayes factor
      3.3.7. Derivation of the log Bayes factor
      3.3.8. Calculation from log model evidences
   

   3.4. Posterior model probability
      3.4.1. Definition
      3.4.2. Derivation
      3.4.3. Calculation from Bayes factors
      3.4.4. Calculation from log Bayes factor
      3.4.5. Calculation from log model evidences
   

   3.5. Bayesian model averaging
      3.5.1. Definition
      3.5.2. Derivation
      3.5.3. Calculation from log model evidences