Joint Probability
Joint probability is the probability of two or more events occurring simultaneously. Understanding joint probability distributions is fundamental to probabilistic machine learning and Bayesian inference.
Understanding Joint Probability
Joint probability measures the likelihood that two or more events occur simultaneously, expressed mathematically as P(A and B). In machine learning, understanding joint probability distributions is fundamental to building probabilistic models, performing Bayesian inference, and designing generative models that learn how multiple variables relate to each other. Bayesian networks explicitly represent joint probabilities through conditional dependencies, while language models implicitly learn the joint probability distribution over sequences of tokens. Joint probability connects closely to conditional probability through the chain rule, which allows complex distributions to be factored into simpler components. In natural language processing, autoregressive language models decompose the joint probability of a text sequence into a product of conditional probabilities, predicting each token given all previous ones. This concept is also central to hypothesis testing and anomaly detection.
Category
Data Science
Is AI recommending your brand?
Find out if ChatGPT, Perplexity, and Gemini mention you when people search your industry.
Check your brand — $9Related Data Science Terms
A/B Testing
A/B testing is an experimental method that compares two versions of a model, prompt, or interface to determine which performs better. In AI, A/B testing helps evaluate model outputs, UI changes, and prompt strategies by measuring user engagement or accuracy.
Annotation
Annotation is the process of adding labels or metadata to raw data to create training datasets for supervised learning. Data annotation can involve labeling images, tagging text, or marking audio segments.
Benchmark
A benchmark is a standardized test or dataset used to evaluate and compare the performance of different AI models. Common benchmarks include MMLU, HumanEval, and ImageNet.
Causal Inference
Causal inference is the process of determining cause-and-effect relationships from data, going beyond mere correlation. AI systems increasingly use causal reasoning to make more robust and interpretable decisions.
Cross-Validation
Cross-validation is a model evaluation technique that splits data into multiple folds, training and testing on different subsets in rotation. K-fold cross-validation provides more reliable performance estimates than a single train-test split.
Data Augmentation
Data augmentation is a technique that artificially increases training dataset size by creating modified versions of existing data. In computer vision, this includes rotations, flips, and color changes; in NLP, it includes paraphrasing and synonym replacement.
Data Drift
Data drift occurs when the statistical properties of production data change over time compared to the training data. Drift can degrade model performance and requires monitoring and retraining strategies to address.
Data Labeling
Data labeling is the process of assigning meaningful tags or annotations to raw data to create supervised learning datasets. High-quality labeled data is essential for training accurate machine learning models.