My tags: topic: data analysis

With this tag

• The three steps of Bayesian data analysis
• General Notation for Statistical Inference
• Bayesian Inference
• Discrete Bayesian Examples – Genetics and Spell Checking (with θ)
• Probability as a Measure of Uncertainty
• Example — Probabilities from Football Point Spreads
• Example — Calibration for Record Linkage
• Some Useful Results from Probability Theory
• Computation and Software
• Bayesian Inference in Applied Statistics
• Estimating a Probability from Binomial Data
• Posterior as a Compromise Between Data and Prior Information
• Summarizing Posterior Inference
• Informative Prior Distributions
• Normal Distribution with Known Variance
• Other Standard Single-Parameter Models
• Informative Prior Distribution for Cancer Rates
• Noninformative Prior Distributions
• Weakly Informative Prior Distributions
• Averaging Over Nuisance Parameters
• Normal Data with a Noninformative Prior Distribution
• Normal Data with a Conjugate Prior Distribution
• Multinomial Model for Categorical Data
• Multivariate Normal Model with Known Variance
• Multivariate Normal with Unknown Mean and Variance
• Example: Bayesian analysis of a bioassay experiment (logistic, nonconjugate)
• Summary of Elementary Modeling and Computation
• Normal Approximations to the Posterior Distribution
• Large-Sample Theory
• Counterexamples to large-sample (asymptotic) Bayesian theorems
• Frequency Evaluations of Bayesian Inferences
• Bayesian interpretations of other statistical methods
• Constructing a Parameterized Prior Distribution
• Exchangeability and hierarchical models
• Bayesian analysis of conjugate hierarchical models
• Normal model with exchangeable parameters
• Example: parallel experiments in eight schools
• Hierarchical modeling applied to a meta-analysis
• Weakly Informative Priors for Variance Parameters
• The Place of Model Checking in Applied Bayesian Statistics
• Do the Inferences from the Model Make Sense?
• Posterior predictive checking
• Graphical posterior predictive checks
• Model checking for the educational testing example
• Measures of predictive accuracy
• Model comparison based on predictive performance
• Model comparison using Bayes factors
• Continuous model expansion
• Implicit assumptions and model expansion: an example
• Bayesian inference requires a model for data collection
• Data-collection models and ignorability
• Sample surveys
• Designed experiments
• Sensitivity and the role of randomization
• Observational studies
• Censoring and truncation
• Bayesian decision theory in different contexts
• Using regression predictions: survey incentives
• Multistage decision making: medical screening
• Hierarchical decision analysis for home radon
• Personal vs. institutional decision analysis
• Numerical integration
• Distributional approximations
• Direct simulation and rejection sampling
• Importance sampling
• How many simulation draws are needed?
• Computing environments
• Debugging Bayesian computing
• Gibbs sampler
• Metropolis and Metropolis-Hastings algorithms
• Using Gibbs and Metropolis as building blocks
• Inference and assessing convergence
• Effective number of simulation draws
• Example: hierarchical normal model
• Efficient Gibbs samplers
• Efficient Metropolis jumping rules
• Further extensions to Gibbs and Metropolis
• Hamiltonian Monte Carlo
• Hamiltonian Monte Carlo for a hierarchical model
• Stan: developing a computing environment
• Finding posterior modes
• Boundary-avoiding priors for modal summaries
• Normal and related mixture approximations
• Finding marginal posterior modes using EM
• Conditional and marginal posterior approximations
• Example: hierarchical normal model (continued)
• Variational inference
• Expectation propagation
• Other approximations
• Unknown normalizing factors
• Conditional modeling
• Bayesian analysis of classical regression
• Regression for causal inference: incumbency and voting
• Goals of regression analysis
• Assembling the matrix of explanatory variables
• Regularization and dimension reduction
• Unequal variances and correlations
• Including numerical prior information
• Regression coefficients exchangeable in batches
• Example: forecasting U.S. presidential elections
• Interpreting a normal prior distribution as extra data
• Varying intercepts and slopes
• Computation: batching and transformation
• Analysis of variance and the batching of coefficients
• Hierarchical models for batches of variance components
• Standard generalized linear model likelihoods
• Working with generalized linear models
• Weakly informative priors for logistic regression
• Overdispersed Poisson regression for police stops
• State-level opinons from national polls
• Models for multivariate and multinomial responses
• Loglinear models for multivariate discrete data
• Aspects of robustness
• Overdispersed versions of standard models
• Posterior inference and computation
• Robust inference for the eight schools
• Robust regression using t-distributed errors
• Notation
• Multiple imputation
• Missing data in the multivariate normal and t models
• Example: multiple imputation for a series of polls
• Missing values with counted data
• Example: an opinion poll in Slovenia
• Example: serial dilution assay
• Example: population toxicokinetics
• Splines and weighted sums of basis functions
• Basis selection and shrinkage of coefficients
• Non-normal models and regression surfaces
• Gaussian process regression
• Example: birthdays and birthdates
• Latent Gaussian process models
• Functional data analysis
• Density estimation and regression
• Setting up and interpreting mixture models
• Example: reaction times and schizophrenia
• Label switching and posterior computation
• Unspecified number of mixture components
• Mixture models for classification and regression
• Bayesian histograms
• Dirichlet process prior distributions
• Dirichlet process mixtures
• Beyond density estimation
• Hierarchical dependence
• Density regression
• Bayesian Data Analysis
• Why Do We Analyze Data?
• The Process of Data Analysis
• CRISP-DM for Data Science
• Big Data: Definition, Characteristics, Evolution, and Business Impact
• The First Step in Knowing Your Data
• IEEE 754 Floating-Point Standard
• Discovering Associations Through Data: From Everyday Patterns to Chicago Taxi Trips (September 2022)
• Taxi Trips – 2022 dataset from the City of Chicago open data portal
• Objective Selection of the Bin Width for a Time Histogram
• Measuring Associations in Data
• Measuring Associations Between Two Continuous Variables
• Correlation Coefficients in Python (Pearson, Spearman, Kendall)
• Karl Pearson
• Harald Cramér
• What Are Statistical Tests?
• Eta Squared (η²): Effect Size in ANOVA
• Understanding Market Baskets and Ideal Customers
• What Can Association Rules Tell Us?
• How Association Rules Are Discovered: Concepts, Scale, Measures, and the Apriori Approach
• Apriori: Frequent Itemsets via the Apriori Algorithm
• association_rules: Generating Association Rules from Frequent Itemsets (mlxtend)
• Cross-Selling
• Stratified Random Sampling
• Linear Congruential Random Number Generator (LCG)
• Partitioning Observations to Train Objective Models
• Putting Similar Observations into Clusters
• Clustering
• Recency, Frequency, and Monetary Value (RFM)
• RFM Analysis
• Creating Segments of Observations for Business Reasons (RFM)
• Least Squares Regression
• Multiple Linear Regression
• Feature Importance in Linear Regression
• Forward Selection: Definition and Core Idea
• Forward Selection and Model Interpretation in Linear Regression
• Understanding Forward and Backward Stepwise Regression
• How Shapley Values Work
• Logistic Regression: Modeling Binary Outcomes via Odds and Log-Odds
• Maximum Likelihood (MLE): Fitting a Distribution to Observed Data
• Assessing Model Fit in Logistic Regression
• Complete and Quasi-Complete Separation in Logistic Regression
• Forward Selection with Nested Models and Deviance Tests
• Interpreting and Assessing a Forward-Selection Logistic Regression Model for College Student Retention
• Motivation of Decision Trees: An Incremental Model of Decision-Making
• The CART Algorithm
• Decision Trees as Piecewise Models and Their Predictive Structure
• How CART Decision Trees Model Interactions
• Cluster Profiling Using Decision Trees
• Using Decision Trees to Explain Clustering Results
• Assessing the Quality of Prediction Models
• Binary Classification Models – Conceptual Framework and Evaluation Metrics
• Nominal Classification Models: Model State and Evaluation Metrics
• Binary Classification Model Evaluation and Threshold Optimization
• Identifying Outliers Using Residuals and Studentized Residuals
• AUC–ROC Curve: Evaluating Classification Model Performance
• Lift Analysis for Direct Mail Campaigns: Concept, Process, and Business Value
• Data Preparation & Analysis