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Marginal distribution
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#	Item
41	Bayesian IV: the normal case with multiple endogenous variables Timothy Cogley and Richard Startz SeptemberAbstract Add to Reading List Source URL: www.econ.ucsb.edu Language: English - Date: 2012-09-17 18:42:01 Regression analysis Simultaneous equations model Gibbs sampling Instrumental variable Errors-in-variables models Conjugate prior Multivariate normal distribution Normal distribution Marginal likelihood Statistics Econometrics Bayesian statistics
42	Bayesian Inference for Non-Markovian Point Processes Peter Guttorp∗ and Thordis L. Thorarinsdottir† November 19, Add to Reading List Source URL: www.stat.washington.edu Language: English - Date: 2010-11-19 13:41:42 Bayesian statistics Estimation theory Conjugate prior Cox process Point process Marginal likelihood Normal distribution Gaussian process Bayes estimator Statistics Stochastic processes Poisson processes
43	J. R. Statist. Soc. B, Part 2, pp. 319–392 Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations Add to Reading List Source URL: www.math.ntnu.no Language: English - Date: 2011-12-21 15:18:36 Estimation theory Markov models Probability theory Variational Bayesian methods Mixture model Normal distribution Marginal likelihood Deviance information criterion Gaussian process Statistics Probability and statistics Bayesian statistics
44	real-world economics review, issue no. 69 subscribe for free Piketty and the limits of marginal productivity theory Lars Pålsson Syll Add to Reading List Source URL: www.paecon.net Language: English - Date: 2014-10-07 07:17:54 Economic theories Economic inequality Capital Welfare economics Thomas Piketty Distribution Neoclassical economics Productivity Economic growth Economics Microeconomics Income distribution
45	Information bounds for Gaussian copulas Add to Reading List Source URL: www.stat.washington.edu Language: English - Date: 2014-03-04 18:56:11 Bayesian statistics M-estimators Maximum likelihood Copula Marginal likelihood Fisher information Likelihood function Multivariate normal distribution Parametric model Statistics Estimation theory Statistical theory
46	Learning Community-based Preferences via Dirichlet Process Mixtures of Gaussian Processes Ehsan Abbasnejad Scott Sanner ANU & NICTA NICTA & ANU Add to Reading List Source URL: users.cecs.anu.edu.au Language: English - Date: 2015-03-17 00:39:18 Markov chain Normal distribution Dirichlet process Bayesian inference Marginal likelihood Conjugate prior Statistics Bayesian statistics Mixture model
47	Using Bayesian Networks and Simulation for Data Fusion and Risk Analysis Martin Neil, Norman Fenton and David Marquez Queen Mary, University of London, and Agena Ltd, UK Abstract. Bayesian networks (BNs) were pioneered t Add to Reading List Source URL: www.agenarisk.com Language: English - Date: 2010-02-23 15:24:03 Mathematical analysis Bayesian statistics Graphical models Numerical analysis Bayesian network Discretization Marginal distribution Independence Bayesian inference Statistics Probability theory Probability and statistics
48	Using Bayesian Networks and Simulation for Data Fusion and Risk Analysis Martin Neil, Norman Fenton and David Marquez Queen Mary, University of London, and Agena Ltd, UK Abstract. Bayesian networks (BNs) were pioneered t Add to Reading List Source URL: www.agena.co.uk Language: English - Date: 2010-02-23 15:24:03 Mathematical analysis Bayesian statistics Graphical models Numerical analysis Bayesian network Discretization Marginal distribution Independence Bayesian inference Statistics Probability theory Probability and statistics
49	High Marginal Tax Rates on the Top 1%? Lessons from a Life Cycle Model with Idiosyncratic Income Risk Fabian Kindermann Dirk Krueger∗ Add to Reading List Source URL: economics.sas.upenn.edu Language: English - Date: 2015-01-23 09:17:26 Taxation Public economics Economic inequality Optimal tax Income tax in the United States Tax Distribution Income tax Progressive tax Income distribution Economics Socioeconomics
50	Decomposing Spatiotemporal Brain Patterns into Topographic Latent Sources Samuel J. Gershman∗ Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA Add to Reading List Source URL: www.cs.columbia.edu Language: English - Date: 2015-03-12 00:16:21 Bayesian statistics Statistical theory Kullback–Leibler divergence Thermodynamics Identifiability Marginal likelihood Gamma distribution Functional magnetic resonance imaging Markov chain Statistics Logic Estimation theory

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