Resources

This space serves as a repository for reference materials, study notes, cheat sheets, and technical guides compiled throughout my studies and research.

Posts below are arranged chronologically—spanning probability theory, machine learning, software tools, and computational notes. You can also use the Archive to browse all articles organized by tags.

Selected notes have also been compiled into standalone PDF documents:

  • Introduction to Probability Theory I (Spring 2020) [PDF]
  • Introduction to Probability Theory II (Fall 2020) [PDF]
  • Introduction to Latent Dirichlet Allocation [PDF]

3.3. Uniform Law Under Random Entropy

empirical process asymptotics Empirical Processes in M-estimation

Using all the lemmas from 3.2 to 3.5, we prove another sufficiency of the uniform law: the vanishing random entropy condition. This condition is fairly weaker than the finite bracketing entropy condition in two senses. First, instead of the (sup-normed) envelope condition, it only requires the envelope to be integrable. In $L^p(Q)$ where $Q$ is a finite measure, this is clearly implied by the envelop condition. In addition, as we already saw before, the vanishing random entropy is implied by the finite bracketing entropy condition.
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