My tags: level: advanced#

With this tag

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
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
Low-pass Filtering
Signal Processing
Time Series
Predictive Parity (Calibration)
Equalized Odds (Fairness)
Equal Opportunity (Fairness)
Demographic Parity (Statistical Parity)
Thompson Sampling (TS) in Bandits (Multi-Armed Bandit Problem (MAB))
Bayesian Decision Theory (BDT)
Bayesian Time Series
Posterior probability of uplift
Gaussian Processes (GPs)
Bayesian Neural Networks (BNNs)
Variational Inference (VI)
MCMC (Markov Chain Monte Carlo)
Sequential Settings
Binomial Likelihood
Posterior belief
Marginal Likelihood (also called The Model Evidence or Integrated Likelihood)
Posterior
Prior Belief (or Prior Probability)
Parameter(s) of Interest
Bayes’ Theorem
Posterior Probability
Sequential Probability Ratio Test (SPRT)
Pocock Method
O’Brien–Fleming (OBF) Method
Ranking Algorithms
Probabilistic Interleaving
Team Draft Interleaving (TDI)
Balanced Interleaving
Causal Impact
Bandit Algorithms
Causal Inference
Temporal autocorrelation (Serial Correlation)
Re-scoring
Drift Detection
AWS SageMaker Endpoints
Cloud Inference with Big Payloads
Cloud Inference
Recalibration
Reweighting
Continuous Retraining
Monitoring Pipelines
Bayesian Correction
Recalibrate Thresholds
Guardrails (in ML & Data Systems)
Model KPIs (Key Performance Indicators)
Windows (in Time-Series)
Autoencoder
Frozen Encoder
Embedding
Representation Shift
Classifier Two-Sample Tests (C2STs)
Energy Distance
Maximum Mean Discrepancy (MMD)
Cardinality in Categorical Data
Categorical Drift
Macro Shifts
Categorical Explosions
Off-Distribution
Model Stability
Feature Values
Four-Fifths (80%) Rule
SLI (Service Level Indicator)
Treatment Cost
Incremental Revenue
Incremental Recovery Rate (IRR)
Incremental Sales
Random Targeting Strategy
Causal ML (Causal Machine Learning)
Cumulative Uplift
Incremental Gain
Total Incremental Benefit (TIB)
Cumulative Incremental Gain (CIG)
Qini Curve
Uplift Score
Uplift Models
Ops Health Dashboard
SLA Breach Rate
SLA (Service Level Agreement)
Prophet — Time Series Forecasting by Facebook (Meta)
LSTM — Long Short-Term Memory Networks
ARIMA (AutoRegressive Integrated Moving Average)
Return Distribution
Value-at-Risk (VaR)
Risk Forecast
Probabilistic Scoring
Full Distribution
Continuous Probabilistic Forecasts
Quantile Forecasts
Point Forecasts
Strictly Proper Scoring Rules
Probability Forecasts
Probabilistic Forecasts
Deterministic forecasts
M-Competitions (Makridakis Competitions)
Forecasting Benchmarks
Seasonal Lag
Simple Baseline Methods
Naïve Baseline Forecast
Forecast Error
Forecasting Competitions
Predicting Percentiles
Prediction Intervals (PI)
Quantile Regression
Quantile Level
Time Series Forecasting
Log-Space
Self-Information of Popularity
Relevance in Recommender Systems
Genre Overlap
Jaccard index
Cosine Similarity of Item Features
Intra-List Diversity (ILD)
Dominating in Recommender Systems
Catalog Coverage
User Coverage
Item Coverage
Diminishing Utility
DCG (Discounted Cumulative Gain)
TREC (Text REtrieval Conference)
Adaptive ECE (Expected Calibration Error with Adaptive Binning)
Maximum Calibration Error (MCE)
Murphy’s Decomposition
Temperature Scaling
Platt Scaling
Isotonic Regression
Underconfident
Overconfident
Risk-Based Decisions
Causal Trees
Uplift Random Forests
Uplift Curve
Causal Effect
Embedding Similarity
Jensen–Shannon (JS) Divergence
Kullback–Leibler (KL) Divergence
Seasonality
Concept Drift
Data Drift
Fair Lending laws
Basel III
High-Stakes Domains
Counterfactual Explanations
LIME (Local Interpretable Model-agnostic Explanations)
SHAP (SHapley Additive exPlanations)
Post-hoc Explainability
Caching
Drift Guardrails
Latency Guardrails
Fairness Guardrails
Dataset Shift
Fairness parity
Bayesian Inference.
Interleaving Tests
Compute budgets
Manual review minutes
Inference Cost (Inference $)
Label Drift (a.k.a. Target Drift)
Covariate Drift (a.k.a. Covariate Shift)
KS shift (Kolmogorov–Smirnov shift)
PSI (Population Stability Index)
Selection Rate
SLOs (Service Level Objectives)
Revenue net of treatment cost
Incremental Conversions
Uplift@k
AUUC (Area Under the Uplift Curve)
Qini Coefficient
SLA Breaches
Continuous Ranked Probability Score (CRPS)
Pinball Loss (a.k.a. Quantile Loss)
Novelty (in Recommender Systems)
Diversity (in Recommender Systems)
Hit Rate (HR)
NDCG (Normalized Discounted Cumulative Gain)
Mean Average Precision (MAP)
Expected Calibration Error (ECE)
Reliability Curves (also called Calibration Curves)
Brier Score
Calibration quality (Model Calibration)
Uplift
Preliminary Estimation for AR Models and the Yule–Walker Equations
Maximum Likelihood Estimation for ARMA Models (Gaussian MLE)
Diagnostics After Fitting a Time Series Model
Order Selection for Time Series Models
ARIMA Models: How Nonstationary Models Are Built from Stationary Ones
SARIMA Models: Seasonal ARIMA
Beyond One-Step Ahead Predictions
Exponential Smoothing Models