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Annealed importance sampling (AIS) is the gold standard for estimating partition functions or marginal likelihoods, corresponding to importance sampling over a path of distributions between a tractable base and an unnormalized target.
On the notion of mean
Andrey Kolmogorov · 1930
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Inequalities
G.H. Hardy, J.E. Littlewood, and G. Pólya · 1953
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A general class of coefficients of divergence of one distribution from another
Syed Mumtaz Ali and Samuel D Silvey · 1966
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Differential geometry of curved exponential families-curvatures and information loss
Shun-ichi Amari · 1982
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Possible generalization of Boltzmann-Gibbs statistics
Constantino Tsallis · 1988
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Equilibrium free-energy differences from nonequilibrium measurements: A master-equation approach
Christopher Jarzynski · 1997
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Simulating normalizing constants: From importance sampling to bridge sampling to path sampling
Andrew Gelman and Xiao-Li Meng · 1998
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Annealed importance sampling
Radford M Neal · 2001
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An introduction to variational calculus in machine learning
Anders Meng · 2004
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Clustering with Bregman Divergences
Arindam Banerjee, Srujana Merugu, Inderjit S Dhillon, and Joydeep Ghosh · 2005
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Divergence measures and message passing
Tom Minka · 2005
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Integration of stochastic models by minimizing α \alpha -divergence
Shun-ichi Amari · 2007
Cited alongside, same era.
Methods of information geometry , volume 191
Shun-ichi Amari and Hiroshi Nagaoka · 2007
Cited alongside, same era.
The minimum description length principle
Peter D Grünwald · 2007
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Conjugate bayesian analysis of the gaussian distribution
Kevin P Murphy · 2007
Cited alongside, same era.
Introduction to nonextensive statistical mechanics: approaching a complex world
Constantino Tsallis · 2009
Cited alongside, same era.
Information geometry of divergence functions
Shun-ichi Amari and Andrzej Cichocki · 2010
Cited alongside, same era.
Deformed algebras and generalizations of independence on deformed exponential families
Hiroshi Matsuzoe and Tatsuaki Wada · 2015
Later among the works it cites.
Information geometry and its applications , volume 194
Shun-ichi Amari · 2016
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Mean, what do you mean?
Miguel de Carvalho · 2016
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Measuring the reliability of MCMC inference with bidirectional Monte Carlo
Roger B Grosse, Siddharth Ancha, and Daniel M Roy · 2016
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The thermodynamic variational objective
Vaden Masrani, Tuan Anh Le, and Frank Wood · 2019
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Normalization problems for deformed exponential families
Hiroshi Matsuzoe, Antonio M Scarfone, and Tatsuaki Wada · 2019
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Geometry of q-exponential family of probability distributions
Shun-ichi Amari and Atsumi Ohara · 2011
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Generalised thermostatistics
Jan Naudts · 2011
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MCMC using Hamiltonian dynamics
Radford M Neal · 2011
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Annealing between distributions by averaging moments
Roger B Grosse, Chris J Maddison, and Ruslan R Salakhutdinov · 2013
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Sandwiching the marginal likelihood using bidirectional Monte Carlo
Roger B Grosse, Zoubin Ghahramani, and Ryan P Adams · 2015
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All in the exponential family: Bregman duality in thermodynamic variational inference
Rob Brekelmans, Vaden Masrani, Frank Wood, Greg Ver Steeg, and Aram Galstyan · 2020
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Connecting the thermodynamic variational objective and annealed importance sampling
Thang Bui · 2020
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An elementary introduction to information geometry
Frank Nielsen · 2020
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Advantages of q-logarithm representation over q-exponential representation from the sense of scale and shift on nonlinear systems
Hiroki Suyari, Hiroshi Matsuzoe, and Antonio M Scarfone · 2020
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