https://www.adventuresinwhy.com/tag/dynamic-programming/Recent content in Dynamic Programming on Adventures in WhyHugoen-usbob@ConvexAnalytics.com (Bob Wilson)bob@ConvexAnalytics.com (Bob Wilson)&copy; Bob Wilson {year}Thu, 20 Aug 2020 00:00:00 +0000https://www.adventuresinwhy.com/post/viterbi-algorithm-decoding/Thu, 20 Aug 2020 00:00:00 +0000bob@ConvexAnalytics.com (Bob Wilson)https://www.adventuresinwhy.com/post/viterbi-algorithm-decoding/This is my second post describing the Viterbi algorithm. As before, our presentation follows Jurafsky and Martin closely, merely filling in some details omitted in the text.https://www.adventuresinwhy.com/post/viterbi-algorithm-likelihood/Tue, 18 Aug 2020 00:00:00 +0000bob@ConvexAnalytics.com (Bob Wilson)https://www.adventuresinwhy.com/post/viterbi-algorithm-likelihood/<p>The Viterbi algorithm is used to find the most likely sequence of states given a sequence of observations emitted by those states and some details of transition and emission probabilities. It has applications in Natural Language Processing like part-of-speech tagging, in error correction codes, and more!</p>https://www.adventuresinwhy.com/post/minimum-edit-distance/Mon, 17 Aug 2020 00:00:00 +0000bob@ConvexAnalytics.com (Bob Wilson)https://www.adventuresinwhy.com/post/minimum-edit-distance/Minimum Edit Distance is defined as the minimum number of edits (delete, insert, replace) needed to transform a source string to a target string. The algorithm uses dynamic programming both to calculate the minimum edit distance and to identify a corresponding sequence of edits.