Probabilistic Information Retrieval

Related lectures (30)

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Covers the fundamental concepts of statistics and their application in data science.

Covers probability theory, distributions, and estimation in statistics, emphasizing accuracy, precision, and resolution of measurements.

Explores statistical models, parameter estimation, and sampling distributions in probability and statistics.

Covers the concept of distribution estimation and the optimization of parameters using different estimators.

Introduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.

Explores independence and conditional probability in probability and statistics, with examples illustrating the concepts and practical applications.

Covers jacamar data analysis, smoking data models, and challenges with log-linear models in visual impairment data.

Explores likelihood in probability, maximum likelihood estimation, and its applications in statistical inference.

Covers binary classification, hypothesis testing, likelihood ratio tests, and decision rules.

Covers maximum likelihood estimation, likelihood function, parameter estimation, and hypothesis testing.