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Random sequence
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#	Item
31	Physica D–107 Data compression and learning in time sequences analysis A. Puglisi a,b,∗ , D. Benedetto c , E. Caglioti c , V. Loreto a,b , A. Vulpiani a,b a Add to Reading List Source URL: samarcanda.phys.uniroma1.it Language: English - Date: 2007-02-19 04:40:42 Mathematical analysis Randomness Statistical theory Thermodynamics Statistical randomness Entropy Random sequence Kullback–Leibler divergence Logarithm Statistics Probability and statistics Information theory
32	Assignment 1: PRNGs 1 Implementation – Part 1 Implement a program prngtest that subjects a sequence of “random” binary Add to Reading List Source URL: grothoff.org Language: English - Date: 2011-06-21 07:45:47
33	19:TOPIC. Cumulants. Just as the generating function M of a random variable X “generates” its moments, the logarithm of M generates a sequence of numbers called the cumulants of X. Cumulants are of int Add to Reading List Source URL: galton.uchicago.edu Language: English - Date: 2002-03-31 23:48:14
34	1 Learning, Regularity, and Compression Overview The task of inductive inference is to find laws or regularities underlying some given set of data. These laws are then used to gain insight Add to Reading List Source URL: homepages.cwi.nl Language: English - Date: 2007-08-21 10:18:33 Information theory Randomness Minimum description length Artificial intelligence Kolmogorov complexity Ray Solomonoff Random sequence Per Martin-Löf Inductive inference Algorithmic information theory Theoretical computer science Mathematics
35	Precise Management of Scratchpad Memories for Localising Array Accesses in Scientific Codes Armin Gr¨ oßlinger University of Passau Department of Informatics and Mathematics Add to Reading List Source URL: www.infosun.fim.uni-passau.de Language: English - Date: 2009-04-06 06:30:34 Computing Computer memory Dynamic random-access memory Sequence container
36	2006 Paper 3 Question 10 Mathematical Methods for Computer Science (a) Suppose that X1 , X2 , . . . is a sequence of random variables. State the Central Limit Theorem, noting any assumptions that you make about the rand Add to Reading List Source URL: www.cl.cam.ac.uk Language: English - Date: 2014-06-09 10:18:10 Confidence interval Econometrics Market research Measurement Normal distribution Central limit theorem Random variable Expected value Prediction interval Statistics Statistical inference Probability theory
37	Semi-Supervised Sequence Labeling with Self-Learned Features Yanjun Qi∗ , Pavel Kuksa† , Ronan Collobert∗ , Kunihiko Sadamasa∗ , Koray Kavukcuoglu‡ and Jason Weston§ ∗ Machine Learning Department, NEC Labs A Add to Reading List Source URL: ronan.collobert.com Language: English - Date: 2009-09-30 13:22:00 Science Semi-supervised learning Natural language processing Co-training Supervised learning Sequence labeling Part-of-speech tagging Conditional random field Hidden Markov model Machine learning Artificial intelligence Statistics
38	Efficient Parameter Variation Sampling for Architecture Simulations Feng Lu Russ Joseph Add to Reading List Source URL: users.eecs.northwestern.edu Language: English - Date: 2011-03-04 16:19:11 Monte Carlo methods Numerical analysis Randomness Statistical inference Probabilistic complexity theory Sampling Low-discrepancy sequence Estimation theory Random sample Statistics Probability and statistics Mathematics
39	Conditional Random Fields with High-Order Features for Sequence Labeling Dan Wu Hai Leong Chieu Nan Ye Add to Reading List Source URL: www.comp.nus.edu.sg Language: English - Date: 2013-03-09 06:31:30 Probability and statistics Machine learning Conditional random field Theoretical computer science Markov chain Hidden Markov model Bayesian network Statistics Markov models Graphical models
40	Semi-Markov Conditional Random Field with High-Order Features Viet Cuong Nguyen Nan Ye Wee Sun Lee National University of Singapore Add to Reading List Source URL: www.comp.nus.edu.sg Language: English - Date: 2013-03-09 06:33:11 Theoretical computer science Statistics Learning Applied mathematics Hidden Markov model Sequence labeling Machine learning Markov models Conditional random field