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Related lectures (7)
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Central Limit Theorem: Characteristic Functions
Explores an alternative proof of the Central Limit Theorem using characteristic functions to show the emergence of the Gaussian distribution.
Simulation & Optimization: Poisson Process & Random Numbers
Explores simulation pitfalls, random numbers, discrete & continuous distributions, and Monte-Carlo integration.
Descriptive Statistics: Hypothesis Testing
Introduces descriptive statistics, hypothesis testing, p-values, and confidence intervals, emphasizing their importance in data analysis.
Coupling of Markov Chains: Ergodic Theorem
Explores the coupling of Markov chains and the proof of the ergodic theorem, emphasizing distribution convergence and chain properties.
Node Degree and Strength
Explores brain node connectivity, node degree, strength, random networks, power law distributions, and the complexity of real networks.
Describing Data: Statistics & Uncertainty
Introduces descriptive statistics, uncertainty quantification, and variable relationships, emphasizing the importance of statistical interpretation and critical analysis.
Diffusion Models in Generative Modeling
Explores diffusion models in generative modeling, covering probability estimation, data generation, and model evaluation.
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