Jason Eisner: Hidden Markov Models

Jason Eisner: Hidden Markov Models

by Jason Eisner

Formal & Physical Sciences AI — Models & Research UYQArtificial intelligence (AI)UYQMMachine learning
🎙 Jason Eisner 👥 4K 📅 December 14, 2025 ⏱ 101 min 👁 27 📄 lecture 🧭 2026-08-16
Available in: English (current) Français

Keywords

Hidden Markov ModelsForward algorithmBackward algorithmViterbi algorithmPart-of-speech tagging

Summary

This lecture by Jason Eisner, from the JHU Summer School on Human Language Technology (2008), provides a comprehensive introduction to Hidden Markov Models (HMMs). Using a pedagogical example of inferring weather from ice cream consumption, Eisner explains the core concepts: states, transitions, emissions, and the Markov assumption. He introduces the trellis representation and demonstrates how to compute the forward probabilities (alpha) efficiently using dynamic programming, avoiding brute-force enumeration of all paths. He then introduces backward probabilities (beta) and shows how combining alpha and beta yields the posterior probability of each state at each time step. The lecture also touches on the Viterbi algorithm for finding the most probable state sequence, and discusses applications to part-of-speech tagging. Throughout, Eisner emphasizes the computational efficiency of these algorithms and provides intuitive explanations of the underlying probabilities.

133 words

Critical Evaluation

Value of the Information & Strength of the Argument

The lecture is highly valuable for its clear and intuitive explanation of HMMs, using a relatable example that makes the mathematics accessible. The argumentation is solid: Eisner builds the model step-by-step, justifies each assumption, and demonstrates the computational trick of dynamic programming with concrete numbers. He also addresses common misconceptions, such as the difference between summing over paths versus summing over all possible sequences. The use of a spreadsheet to illustrate the calculations adds practical value. The lecture is well-structured and the reasoning is rigorous, making it an excellent educational resource.

Scientific Rigor, Source Quality, Title Accuracy

The lecture is scientifically rigorous, with precise definitions and derivations. Eisner does not cite external sources, but the content is based on established theory in statistical NLP. The title accurately reflects the content. The lecture is part of a reputable academic program (JHU CLSP), which enhances its credibility. No comments were provided for analysis.

160 words

Title / Content Match

The title accurately reflects the content, which is a detailed lecture on Hidden Markov Models.

Quality & Reliability

9/10

Lecture by a leading researcher in NLP, part of a JHU summer school, with rigorous mathematical exposition and clear pedagogical examples. The content is well-structured and technically accurate, though it is a lecture rather than peer-reviewed publication.

Key Moments

Markers derived by PSI from the transcript: the creator did not define chapters.

Contribution & Novelties

This lecture provides a clear and accessible introduction to HMMs, with a strong emphasis on the underlying algorithms and their computational efficiency. It is particularly valuable for its pedagogical approach, using a concrete example to illustrate abstract concepts. The lecture also highlights the importance of considering both past and future context in sequence modeling.

Pour aller plus loin :

112 words

Radar Profile

The radar profile shows high scores across all dimensions, indicating a well-rounded and reliable educational resource. The lecture excels in information quantity and quality, with a strong technical level and high reliability.

Reliability 9/10