PPT-Note-level Music Transcription by Maximum Likelihood Sampling

Zhiyao Duan ¹ amp David Temperley ² Department of Electrical and Computer Engineering Eastman School of Music University of Rochester Presentation at ISMIR 2014 Taipei Taiwan October 28 2014. : Session 1. Pushpak Bhattacharyya. Scribed by . Aditya. Joshi. Presented in NLP-AI talk on 14. th. January, 2014. Phenomenon/Event could be a linguistic process such as POS tagging or sentiment prediction.. How would we select parameters in the limiting case where we had . ALL. the data? .  . k. . →. l . k. . →. l . . S. l. ’ . k→ l’ . Intuitively, the . actual frequencies . of all the transitions would best describe the parameters we seek . Lecture XX. Reminder from Information Theory. Mutual Information: . . Conditional Mutual Information: . . Entropy: Conditional Mutual Information: . . Scoring Maximum Likelihood Function. When scoring function is the Maximum Likelihood, the model would make the data as probable as possible by choosing the graph structure that would produce the highest score for the MLE estimate of the parameter, we define:. See Davison Ch. 4 for background and a more thorough discussion.. Sometimes. See last slide for copyright information. Maximum Likelihood. Sometimes. Close your eyes and differentiate?. Simulate Some Data: True α=2, β=3. Machine Learning. Last Time. Support Vector Machines. Kernel Methods. Today. Review . of Supervised Learning. Unsupervised . Learning . (. Soft) K-means clustering. Expectation Maximization. Spectral Clustering. Published courtesy of the CEM . FOAMed. Network. http://. www.cemfoamed.co.uk. /portfolio/diagnostics-in-. em. /. Everything we do in a patient assessment is a test. Including questions we ask. Test thresholds. Selection of Training Areas. DN’s of training fields plotted on a “scatter” diagram in two-dimensional feature space. Band 1. Band 2. from. Lillesand & Kiefer. Classification Algorithms/Decision Rules. Maximum. Likelihood. Estimation. Probabilistic. Graphical. Models. Learning. Biased Coin Example. Tosses are independent of each other. Tosses are sampled from the same distribution (identically distributed). Donald A Pierce, Emeritus, OSU Statistics. and. Ruggero. . Bellio. , . Univ. of Udine. Slides and working paper, other things are at. : . . http://www.science.oregonstate.edu/~. piercedo. Slides and paper only are at: . May 29 – June 2, 2017. Fort Collins, Colorado. Instructors:. Charles Canham. And. Patrick Martin. Daily Schedule. Morning. 8:30 – 9:30 Lecture. 9:30 – 10:30 Case Study and Discussion. 10:30 – 12:00 Lab. Motivation. Past lectures have studied how to infer characteristics of a distribution, given a fully-specified Bayes net. Next few lectures: . where does the Bayes net come from. ?. Win?. Strength. Opponent Strength. 0020406081050709Erosion widthdepth ratio0020406081080911112LikelihoodSediment flow factor00204060812878128178LikelihoodD50mm00204060810010203LikelihoodPorosity 0020406081192123LikelihoodDensity kN/m30 Le Gal F, Gault E, Ripault M, Serpaggi J, Trinchet J, Gordien E, et al. Eighth Major Clade for Hepatitis Delta Virus. Emerg Infect Dis. 2006;12(9):1447-1450. https://doi.org/10.3201/eid1209.060112. Professional medical transcription services provided by SpectraScribe offer a streamlined solution for researchers seeking to extract meaningful insights from various research methodologies.