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Dirichlet-multinomial distribution
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Related lectures (32)
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Topic Models
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Introduces topic models, covering clustering, GMM, LDA, Dirichlet distribution, and variational inference.
Probabilistic Topic Models: Latent Dirichlet Allocation
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Explores Latent Dirichlet Allocation, a probabilistic topic model for document clustering and analysis using distributions over words and topics.
Topic Models: Understanding Latent Structures
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Explores topic models, Gaussian mixture models, Latent Dirichlet Allocation, and variational inference in understanding latent structures within data.
Bayesian Networks: Fundamentals and Applications
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Covers the fundamentals of Bayesian Networks and their applications in probabilistic topic modeling.
Types of Variables and Multinomial Distribution
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Introduces types of variables, multinomial distribution, data characteristics, shapes of densities, correlation, and data visualization methods.
Asymmetric Parametric Models: Extremal Coefficient
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Explores asymmetric parametric models and the extremal coefficient in extreme value dependence.
Data Analysis: Correlation Measures
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Covers the basics of data analysis, focusing on statistical concepts and correlation measures.
Insurance Risk Modeling
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Covers the modeling of insurance risk, discussing joint distributions and the impact of accidents.
Partitioning Equilibrium
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Covers partitioning equilibrium, sorption isotherms, and the role of natural organic matter in environmental chemistry.
Document Analysis: Topic Modeling
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Explores document analysis, topic modeling, and generative models for data generation in machine learning.
Deep Generative Models
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Covers deep generative models, including LDA, autoencoders, GANs, and DCGANs.
Copulas and Margins
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Explores copulas with different margins and their joint distributions, including concepts like concordance probability and extremal dependence.
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