Learning from Counts: Transition and Emission Tables from a Labelled Logbook, with Add-One Smoothing
This lesson is part of the course
In this lesson
You count weather transitions with COUNTIFS on two shifted ranges, turn them into transition probabilities, estimate emission probabilities from labelled days and use add-one smoothing so that an outcome never seen in a state still gets a small chance.
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Lessons in this course
- Markov Chains Refresher: Transition Tables, Rows That Sum to 1 and Two-Step Probabilities Locked
- Hidden States & Emissions: The Joint Probability of a Weather Path and a Logbook Locked
- The Forward Algorithm: α Tables Step by Step and the Probability of a Logbook Locked
- Viterbi Decoding: δ Tables with MAX, Back-Pointers and the Best Path by Backtracking Locked
- Log Probabilities: Underflow, LN Sums Instead of Products and Comparing Paths Locked
- Backward & Smoothing: β Tables and the Posterior Probability of Each Day's Weather Locked
- Learning from Counts: Transition and Emission Tables from a Labelled Logbook, with Add-One Smoothing Locked
- Toy Part-of-Speech Tagging: Three Hidden Tags, Viterbi on a Short Sentence, Accuracy and a Posterior Chart Locked