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Likelihood
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91	Spectral learning of linear dynamics from generalised-linear observations with application to neural population data Lars Buesing∗ , Jakob H. Macke∗,† , Maneesh Sahani Gatsby Computational Neuroscience Unit Add to Reading List Source URL: www.mackelab.org Language: English - Date: 2016-08-04 15:02:44 Statistics Statistical theory Estimation theory Actuarial science Expectationmaximization algorithm Missing data Maximum likelihood estimation Variational Bayesian methods Linear regression Normal distribution Covariance Regression analysis
92	RMM Vol. 2, 2011, 48–66 Special Topic: Statistical Science and Philosophy of Science Edited by Deborah G. Mayo, Aris Spanos and Kent W. Staley http://www.rmm-journal.de/ Stephen Senn Add to Reading List Source URL: www.rmm-journal.com Language: English - Date: 2011-09-26 02:42:15 Statistics Statistical theory Statistical inference Knowledge Philosophy of science Psychometrics Probability interpretations Bayesian Inverse probability Likelihood function Ronald Fisher Foundations of statistics
93	Chapter 6 Parameter Estimation Take a random variable x described by a pdf f (x): the sample space is defined to be the set of all possible values of x. The set of n independent measurements of the random variable x, {x Add to Reading List Source URL: ihp-lx.ethz.ch Language: English - Date: 2015-03-31 05:26:54 Statistics Estimation theory Statistical theory Probability distributions Statistical inference Bias of an estimator Maximum likelihood estimation Efficient estimator Estimator Uniform distribution Normal distribution Exponential distribution
94	Mean Field Variational Approximations in Continuous-Time Markov Processes A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science Add to Reading List Source URL: www.cs.huji.ac.il Language: English - Date: 2015-08-10 08:23:20 Bayesian statistics Statistics Graphical models Probability Markov models Bayesian network Probability distribution Markov chain Expectation propagation Approximate inference Likelihood function Hidden Markov model
95	Scalable Training of Mixture Models via Coresets Dan Feldman MIT Matthew Faulkner Add to Reading List Source URL: eew.caltech.edu Language: English - Date: 2012-05-15 11:47:37 Statistics Probability Maximum likelihood estimation Probability distributions Bayesian statistics Expectationmaximization algorithm Mixture model Normal distribution K-means clustering Likelihood function Data stream clustering Gamma distribution
96	Structural Graphical Lasso for Learning Mouse Brain Connectivity Sen Yang Qian Sun Add to Reading List Source URL: peterwonka.net Language: English - Date: 2015-11-20 08:10:54 Statistics Estimation theory Inverse problems Dimension reduction Maximum likelihood estimation Lasso Graphical lasso Feature selection Estimation of covariance matrices Regularization Matrix Loss function
97	(Karsten Tabelow , Henning U. Voss , Jörg Polzehl Local estimation of noise standard deviation in Add to Reading List Source URL: www.wias-berlin.de Language: English - Date: 2014-06-02 11:30:03 Statistics Data analysis Diffusion MRI Imaging Magnetic resonance imaging Neuroimaging Tensors Maximum likelihood estimation Standard deviation Estimation theory
98	C:/Users/Utente/Desktop/PhD/CAL_tex/ISMIR2010_DTM/ISMIR2010_DTM.dvi Add to Reading List Source URL: eceweb.ucsd.edu Language: English - Date: 2015-07-31 19:02:39 Statistics Estimation theory Physics Mechanical engineering Cluster analysis Machine learning Mixture model Expectationmaximization algorithm Maximum likelihood estimation Work
99	Maximum Likelihood Estimation of Dirichlet Distribution Parameters Jonathan Huang Abstract. Dirichlet distributions are commonly used as priors over proportional data. In this paper, I will introduce this distribution, d Add to Reading List Source URL: jonathan-huang.org Language: English - Date: 2014-09-08 21:47:30 Statistics Mathematical analysis Probability Probability distributions Gamma distribution Dirichlet distribution Exponential family Beta distribution Mixture model Multinomial distribution Likelihood function Maximum likelihood estimation
100	Ann Inst Stat Math:469–490 DOIs10463Second-order asymptotic comparison of the MLE and MCLE of a natural parameter for a truncated exponential family of distributions Add to Reading List Source URL: www.ism.ac.jp Language: English - Date: 2016-06-28 03:34:43 Statistics Estimation theory Statistical theory Maximum likelihood estimation Efficient estimator Efficiency Estimator Asymptotic theory Ole Barndorff-Nielsen Likelihood function Two-step M-estimators involving MLE Generalized method of moments

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