Probabilistic Information Retrieval

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Related lectures (30)

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Probabilistic Information Retrieval

Covers probabilistic information retrieval, query likelihood models, language modeling, and relevance feedback algorithms.

Probabilistic Retrieval Models

Covers probabilistic retrieval models, evaluation metrics, query likelihood, user relevance feedback, and query expansion.

Estimators and Confidence Intervals

Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.

Statistical Significance: Maximum Likelihood Estimation and Confidence Intervals

Explores type I and type II errors, critical values, and confidence intervals in statistical significance.

Probabilistic Retrieval

Covers Probabilistic Information Retrieval, modeling relevance as a probability, query expansion, and automatic thesaurus generation.

Statistical Theory: Maximum Likelihood Estimation

Explores the consistency and asymptotic properties of the Maximum Likelihood Estimator, including challenges in proving its consistency and constructing MLE-like estimators.

Spin Glasses and Bayesian Estimation

Covers the concepts of spin glasses and Bayesian estimation, focusing on observing and inferring information from a system closely.

Elements of Statistics: Memorylessness, Stationary Processes, Estimation using MLE

Explores memorylessness in distributions, stationary processes, and estimation using MLE.

Confidence Intervals: Definition and Estimation

Explains confidence intervals, parameter estimation methods, and the central limit theorem in statistical inference.

System Modeling Languages

Explores the significance of System Modeling Languages like OPM, SysML, and Modelica in modern Systems Engineering.