Approximate Query Processing: BlinkDB

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Related lectures (30)

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Normal Distribution: Characteristics and Examples

Covers the characteristics and importance of the normal distribution, including examples and treatment scenarios.

Sampling: Inference and Statistics

Explores sampling, inferential statistics, and effective experimentation in statistics.

Sampling strategies

Explores research process, variable types, causality vs correlation, and sampling strategies.

Sampling: Inference and Statistics

Explores sampling in inferential statistics, emphasizing the impact of sample size and randomness on inference accuracy.

Natural Language Generation: Decoding & Training

Explores challenges in natural language generation, decoding algorithms, training issues, and reward functions.

Density of States and Bayesian Inference in Computational Mathematics

Explores computing density of states and Bayesian inference using importance sampling, showcasing lower variance and parallelizability of the proposed method.

Monte Carlo: Optimization and Estimation

Explores optimization and estimation in Monte Carlo methods, emphasizing Bayes-optimal groups and estimators.

Generative Models: Self-Attention and Transformers

Covers generative models with a focus on self-attention and transformers, discussing sampling methods and empirical means.

Generative Models: Boltzmann Machine

Covers generative models, focusing on Boltzmann machines and constrained maximization using Lagrange multipliers.

Markov Chains and Algorithm Applications

Covers Markov chains and their applications in algorithms, focusing on Markov Chain Monte Carlo sampling and the Metropolis-Hastings algorithm.