texReferences.bib

% List of journal Short (search and replace if needed)
% -------------------------------------------------------------------------------------------
% JMLR 						--- The journal of Machine Learning Research 
% NIPS 						--- Advances in Neural Information Processing Systems
% IEEE Trans. Inf. Theory	--- IEEE Transactions on Information Theory
% IEEE Trans. Sig. Proc.    --- IEEE Transactions on Signal Processing
% Found. and Tr. in Mach. Learn. --- Foundations and Trends in Machine Learning
% MERL 						--- Mitsubishi Electric Research Laboratories 
% Journal of Comp. and Graph. Stats. --- Journal of Computational and Graphical Statistics
%
% -- proceedings
% AISTATS 		--- Proceedings of the International Conference on Artificial Intelligence and Statistics
% ICIF 			--- International Conference on Information Fusion
% UAI			--- Proceedings of the Conference on Uncertainty in Artificial Intelligence

% ------ official ones ---------------------------------------------------------------------
% Ann. Inst. Stat. Math. 	--- Annals of the Institute of Statistical Mathematics
% Commun. ACM 				--- Communications of the ACM
% ------------------------------------------------------------------------------------------

% A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A A
@article{amari98,
	author={Amari, Shun-Ichi},
	title={Natural Gradient Works Efficiently in Learning},
	journal={Neur. Comp. },
	volume={10},
	pages={251-276},
	year={1998}}
@article{amari98b,
	author={Amari, Shun-Ichi and Douglas, Scott C.},
	title={Why Natural Gradient},
	journal={IEEE Tech. Note},
	year={1998}}
@book{anderson79,
	author = {Anderson, Brian and Moore, John},
	title = {Optimal Filtering},
	publisher = {Prentice Hall}, 
	year = {1979}}
@article{arulampalam02,
	author = {Arulampalam, Sanjeev and Maskell, Simon and Gordon, Neil and Clapp, Tim},
	title = {A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking},
	journal = {IEEE Trans. Sig. Proc.},
	pages = {174--188},
	volume = {50},
	number = {2},
	year = {2002}}

% B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B
@article{battey15,
        author = {Battey, H. and Fan, J. and Liu, H. and Lu, J. and Zhu, Z.
        	},
        title = "{Distributed Estimation and Inference with Statistical Guarantees}",
        journal = {arXiv:1509.05457},
        year = {2015},
}
@article{bardenet17,
	author={Bardenet, R\'emi and Doucet, Arnaud and Holmes, Chris},
	title={On Markov Chain Monte Carlo Methods for Tall Data},
	journal={JMLR},
	year={2017}}
@article{barp18,
    author = {Alessandro Barp and  Francois-Xavier Briol and  Anthony D. Kennedy and  Mark Girolami},
    title = {Geometry and Dynamics for Markov Chain Monte Carlo},
    journal = {Ann.\ Rev.\ Stat.\ App.},
    volume = {5},
    number = {1},
    year = {2018}   
}
@article{barthelme11,
	title={ABC-EP: Expectation Propagation for Likelihood-free Bayesian Computation},
	author={Barthelme, Simon and Chopin, Nicolas},
	journal={ICML},
	year={2011}}
@article{beal03,
	author = {Beal, Matthew J.},
	title = {Variational Algorithms for Approximate Bayesian Inference},
	journal={Gatsby PhD thesis},
	year={2003}}
@article{beck03,
	author={Beck, Amir and Teboulle, Marc},
	title={Mirror Descent and Nonlinear Projected Subgradient Methods for Convex Optimization},
	journal={Operations Research Letters},
	volume = {31},
	pages={167-175},
	year={2003}}
@book{bellman57,
	author={Bellman, Richard E.},
	title={Dynamic Programming},
	publisher={Princeton University Press},
	year={1957},
}
@article{bengtsson08,
	author={Bengtsson, Thomas and Bickel, Peter and Li, Bo},
	title={Curse-of-dimensionality Revisited: Collapse of the Particle Filter in Very Large Scale Systems},
	journal={Inst. Math. Stats. Coll. },
	pages={316--334},
	year={2008}
}
@article{bernton17,
	author={Bernton, Espen and Jacob, Pierre E. and Gerber, Matthieu and Robert, Christian P.},
	title={Inference in Generative Models using the Wasserstein Distance},
	journal={arXiv:1701.05146},
	year={2017}
}
@article{betancourt17,
	author={Michael Betancourt},
	title={A Conceptual Introduction to Hamiltonian Monte Carlo},
	journal={arXiv:1701.02434},
	year={2017}
}
@article{bierkens16,
	author={Bierkens, Joris and Fearnhead, Paul and Roberts, Gareth},
	title={The Zig-Zag Process and Super-Efficient Sampling for Bayesian Analysis of Big Data},
	journal={arXiv:1607.03188},
	year={2016}
}
@article{bierkens17,
	author={Bierkens, Joris and Bouchard-C\^ot\'e, Alexandre and Doucet, Arnaud and Duncan, Andrew B. and Fearnhead, Paul and Lienart, Thibaut and Roberts, Gareth and Vollmer, Sebastian J.},
	title={Piecewise Deterministic Markov Processes for Scalable Monte Carlo on Restricted Domains},
	journal={arXiv:1701.04244},
	year={2017}
}
@book{blake11,
	author={Blake, Andrew and Kohli, Pushmeet and Rother, Carsten},
	title={Markov Random Fields for Vision and Image Processing},
	publisher={The MIT Press},
	year={2011}}	
@article{blei16,
	author = {Blei, David M. and Kucukelbir, Alp and McAuliffe, Jon D.},
	title = {Variational Inference: A Review for Statisticians},
	journal = {arXiv:1601.00670v4},
	year={2016}}
@article{bouchard15,
	author={Bouchard-C\^ot\'e, Alexandre and Vollmer, Sebastian J. and Doucet, Arnaud},
	title={The Bouncy Particle Sampler: A Non-Reversible Rejection-Free Markov Chain Monte Carlo Method},
	journal={arXiv:1510.02451},
	year={2015}}
@article{bresler86,
	author={Bresler, Yoram},
	title={Two Filter Formulae for Discrete Time Nonlinear Bayesian Smoothing},
	journal={IJC},
	pages={629--641},
	volume={43},
	issue={2},
	year={1986}}	
@article{briers05,
	author = {Briers, Mark and Doucet, Arnaud and Singh, Sumeetpal S.},
	journal = {ICIF},
	pages = {705--711},
	title = {Sequential Auxiliary Particle Belief Propagation},
	volume = {1},
	year = {2005}}
@article{briers10,
	author = {Briers, Mark and Doucet, Arnaud and Maskell, Simon},
	journal = {AISM},
	number = {1},
	pages = {61--89},
	title = {Smoothing Algorithms for State-Space Models},
	volume = {62},
	year = {2010}}
@book{brown86,
	author={Brown, Lawrence D.},
	title={Fundamentals of Statistical Exponential Families},
	publisher={Inst. Math. Stats. },
	year={1986}}
@article{bunch14,
	author = {Bunch, Pete and Godsill, Simon},
	title = {The Progressive Proposal Particle Filter: Better Approximations to the Optimal Importance Density},
	journal = {arXiv:1401.2791},
	year = {2014}}
	
% C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C
@article{caflisch98,
	author={Caflisch, Russel},
	title={Monte Carlo and Quasi-Monte Carlo Methods},
	pages={1--49},
	journal={Acta Numerica},
	year={1998}
}
@article{carpenter16,
	author={Carpenter, Bob and Gelman, Andrew and Hoffman, Matthew D. and Lee, Daniel and Goodrich, Ben and Betancourt, Michael and Brubaker, Marcus A. and Guo, Jiqiang and Li, Peter and Riddell, Allen},
	title={Stan: A Probabilistic Programming Language},
	journal={Journ. Stats. Soft. },
	year={2016}}
@article{chopin04,
	author={Chopin, Nicolas},
	title={Central Limit Theorem for Sequential Monte Carlo Methods and its Application to Bayesian Inference},
	journal={Ann. Stats.},
	volume={32},
	number={6},
	pages={2385--2411},
	year={2004}}
@article{clifford90,
	author={Clifford, Peter},
	title={Markov Random Fields in Statistics},
	journal={Cambridge Math. Inst. },
	year={1990}}
@article{crick03,
	author={Crick, Christopher and Pfeffer, Avi},
	title={Loopy Belief Propagation as a Basis for Communication in Sensor Networks},
	journal={UAI},
	pages={159--166},
	year={2003}}
@article{crisan02,
	author={Crisan, Dan and Doucet, Arnaud},
	title={A Survey of Convergence Results on Particle Filtering Methods for Practitioners},
	journal={IEEE Trans. Sig. Proc.},
	volume={50},
	number={3},
	pages={736--746},
	year={2002}}

% D D D D D D D D D D D D D D D D D D D D D D D D D D D D D D D D D D D D D D D
@book{davis75,
	author={Davis, Philip J. and Rabinowitz, Philip},
	title={Methods of Numerical Integration},
	publisher={New York, Academic Press},
	year={1975}
}
@article{dean12,
	author={Dean, Jeffrey and Corrado Greg S. and Monga, Rajat and Chen, Kai and   Devin, Matthieu and Le, Quoc V. and  Mao, Mark Z. and Ranzato, Marc'Aurelio  and  Senior, Andrew and  Tucker, Paul and  Yang, Ke and  Ng, Andrew Y.},
	title={Large Scale Distributed Deep Networks},
	journal={NIPS},
	year={2012}}
@article{dehaene15,
	author={Dehaene, Guillaume and Barthelme, Simon},
	title={Expectation Propagation in the Large-data Limit},
	journal={arXiv:1503.08060},
	year={2015}}
@article{delmoral06,
	author={Del Moral, Pierre and Doucet, Arnaud and Jasra, Ajay},
	title={Sequential Monte Carlo Samplers},
	journal={JRSSB},
	number={68},
	pages={411--436},
	year={2006}}
@article{delmoral09,
	author = {Del Moral, Pierre and Doucet, Arnaud and Singh, Sumeetpal},
	title = {Forward Smoothing using Sequential Monte Carlo},
	journal = {Cambridge U. TR},
	year = {2009}}
@article{doucet00,
	author = {Doucet, Arnaud and Godsill, Simon and Andrieu, Christophe},
	title = {On Sequential Monte Carlo Sampling Methods for Bayesian Filtering},
	journal = {Stats. and Comp. }, 
	year = {2000}}
@article{doucet11,
	author = {Doucet, Arnaud and Johansen, Adam},
	title = {A Tutorial on Particle Filtering and Smoothing: Fifteen years later},
	journal = {Tutorial},
	year = {2011}}

% E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E E 
@article{elidan06,
	author={Elidan, Gal and McGraw, Ian and Koller, Daphne},
	journal={UAI},
	title={Residual Belief Propagation: Informed Scheduling for Asynchronous Message Passing},
	year={2006}}
@article{eslami14,
	author={Eslami, SM Ali and Tarlow, Daniel and Kohli, Pushmeet and Winn, John},
	title={Just-In-Time Learning for Fast and Flexible Inference},
	journal={NIPS},
	year={2014}}

% F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F
@article{fearnhead10,
	author = {Fearnhead, Paul and Wyncoll, David and Tawn, Jonathan},
	journal = {Biometrika},
	Month = {June},
	number = {2},
	pages = {447--464},
	title = {A Sequential Smoothing Algorithm with Linear Computational Cost},
	volume = {97},
	year = {2010}}
@article{felzenszwalb04,
	author={Felzenszwalb, Pedro F. and Huttenlocher, Daniel P.},
	title = {Efficient Graph-Based Image Segmentation},
	journal = {IJCV},
	volume={59},
	number={2},
	year={2004}}
@article{freeman00,
	title={Learning Low-level Vision},
	author={Freeman, W. T. and Pasztor, E. C. and Carmichael, O. T.}, 
	journal={IJCV},
	volume={40},
	number={1},
	pages={25--47},
	year={2000}}

% G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G G
@article{gales07,
	title={The Application of Hidden Markov Models in Speech Recognition},
	author={Gales, Mark and Young, Steve},
	journal={Found. and Tr. in Sig. Proc.},
	volume={1},
	number={3},
	pages={195--304},
	year={2007}
}
@book{gelman13,
	title={Bayesian Data Analysis, 3d ed.},
	author={Andrew Gelman and J. B. Carlin and H. S. Stern and D. B. Dunson and  Aki Vehtari and D. B. Rubin},
	publisher={Chapman \& Hall},
	year={2013}}
@article{gelman14,
	author = {Andrew Gelman and Aki Vehtari and Pasi Jyl\"{a}nki and Christian Robert and Nicolas Chopin and John P. Cunningham},
	title = {Expectation Propagation as a Way of Life},
	journal = {arXiv:1412.4869},
	year = {2014}}
@article{geyer05,
	title={Markov Chain Monte Carlo Lecture Notes},
	author={Geyer, Charles},
	journal={Lecture Notes},
	year={2005}}
@article{ghahramani01,
	author={Ghahramani, Zoubin},
	title={An Introduction to Hidden Markov Models and Bayesian Networks},
	journal={IJPRAI},
	volume={15},
	number={1},
	pages={9--42},
	year={2001}
}
@article{ghahramani15,
	author={Ghahramani, Zoubin},
	title={Probabilistic Machine Learning and Artificial Intelligence},
	journal={Nature},
	year={2015}
}
@article{godsill04,
	author = {Godsill, Simon and Doucet, Arnaud and West, Mike},
	title = {Monte Carlo Smoothing for Nonlinear Time Series},
	journal = {JASA},
	year = {2004}}
@article{green15,
	author = {Green, Peter J. and Latuszynski, Krzysztof and Pereyra, Marcelo and Robert, Christian P.},
	journal = {Stat. Comput. },
	title={Bayesian Computation: a Summary of the Current State, and Samples Backwards and Forwards},
	year={2015}}

% H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H
@article{hasenclever16,
	author={Hasenclever, Leonard and Webb, Stefan and Lienart, Thibaut and Vollmer, Sebastian and Lakshminarayanan, Balaji and Blundell, Charles and Teh, Yee Whye},
	title={Distributed Bayesian Learning with Stochastic Natural Gradient Expectation Propagation and the Posterior Server},
	journal={JMLR},
	number={18},
	pages={1--37},
	year={2017}
	}
@article{heess13,
	author={Heess, Nicolas and Tarlow, Daniel and Winn, John},
	title={Learning to Pass Expectation Propagation Messages},
	journal={NIPS},
	year={2013}}
@article{herbrich05,
	author={Herbrich, Ralf},
	title={On Gaussian Expectation Propagation},
	journal={MSR TR},
	year={2005}
}
@article{herbrich06,
	author={Herbrich, Ralf and Minka, Tom and Graepel, Thore},
	title={TrueSkill: A Bayesian Skill Rating System},
	journal={NIPS},
	year={2006}}
@article{hernandez13,
	author={Hernandez-Lobato, Daniel and Hernandez-Lobato, Jose Miguel and Dupont, Pierre},
	title={Generalized Spike-and-Slab Priors for Bayesian Group Feature Selection Using Expectation Propagation},
	journal={JMLR},
	volume={14},
	page={1891--1945},
	year={2013}}
@article{hernandez15,
	author={Hernandez-Lobato, Jose Miguel and Li, Yingzhen and Rowland, Mark and Hernandez-Lobato, Daniel and Bui, Thang and Turner, Richard E.},
	title={Black-box $\alpha$-divergence Minimization},
	journal={arXiv:1511.03243},
	year={2015}}
@article{heskes03,
	author={Heskes, Tom and Zoeter, Onno},
	title={Extended Version of ``Expectation Propagation for Approximate Inference in Dynamic Bayesian Networks''},
	journal={UAI},
	year={2003}
	} 
@article{heskes04,
	title={On the Uniqueness of Loopy Belief Propagation Fixed Points},
	author={Heskes, Tom},
	journal={Neural Comp. },
	volume={16},
	pages={2379--2413},
	year={2004}}
@article{heskes05,
	author={Heskes, Tom and Opper, Manfred and Wiegerink, Wim and Winther, Ole},
	title={Approximate Inference Techniques with Expectation Constraints},
	journal={JSM},
	number={11},
	year={2005}
}
@article{hoffman13,
	author={Hoffman, Matthew D. and Blei, David M. and Wang, Chong and Paisley, John},
	title = {Stochastic Variational Inference},
	journal={JMLR},
	volume={14},
	pages={1303--1347},
	year={2013}}
@article{hoffman14,
	author={Hoffman, Matthew D. and Gelman, Andrew},
	title={The no-U-turn sampler: Adaptively setting path lengths in Hamiltonian Monte Carlo},
	journal={JMLR},
	volume={15},
	year={2014}}
@article{hol06,
	author={Hol, Jeroen D. and Sch\"on, Thomas B. and Gustafsson, Fredrik},
	title={On Resampling Algorithms for Particle Filters},
	journal={IEEE Proc. NSSP},
	pages={79--82},
	year={2006}}
@article{hurzeler98,
	title={Monte Carlo Approximations for General State-Space Models},
	author={H\"urzeler, Markus and K\"unsch, Hans R.},
	journal={JCGS},
	volume=7,
	number=2,
	pages={175--193},
	year={1998}
}	
	
% I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I
@article{ihler05,
	author = {Ihler, Alexander T. and Fisher, John W. and Willsky, Alan S.},
	journal = {JMLR},
	title = {Loopy Belief Propagation: Convergence and Effects of Message Errors},
	number = {6},
	year = {2005},
	pages = {905--936}}
@article{ihler05b,
	author={Ihler, Alexander T. and Fisher, John W. and Moses, Randolph L. and Willsky, Alan S.},
	title={Nonparametric Belief Propagation for Self-localization of Sensor Networks},
	journal={IEEE Sel. Ar. Comm.},
	volume={23},
	pages={809--819},
	year={2005}}
@article{ihler09,
	author = {Alexander T. Ihler and David A. McAllester},
	journal = {AISTATS},
	pages = {256--263},
	title = {Particle Belief Propagation},
	year = {2009}}

% J J J J J J J J J J J J J J J J J J J J J J J J J J J J J J 
@article{jitkrittum15,
	author={Jitkrittum, Wittawat and Gretton, Arthur and Heess, Nicolas and Eslami, SM Ali and Lakshminarayanan, Balaji and Sejdinovic, Dino and Szabo, Zoltan},
	title={Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages},
	journal={NIPS},
	year={2015}}

% K K K K K K K K K K K K K K K K K K K K K K K K K K K K K K K K K K K K K K K
@article{kitagawa94,
	author = {Kitagawa, Genshiro},
	title = {The Two-Filter Formula for Smoothing and an Implementation of the Gaussian-sum Smoother},
	journal={AISM},
	year={1994}}
@article{kitagawa96,
	author = {Kitagawa, Genshiro},
	title = {Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models},
	journal = {JCGS},
	year = {1996}}
@article{klaas05,
	author = {Klaas, Mike and Lang, Dustin and de Freitas, Nando},
	title = {Fast Maximum a posteriori in Monte Carlo State Spaces},
	journal = {AISTATS},
	year = {2005}}
@article{klaas06,
	author = {Klaas, Mike and Briers, Mark and de Freitas, Nando and Doucet, Arnaud and Maskell, Simon and Lang, Dustin},
	title = {Fast Particle Smoothing: If I Had a Million Particles},
	journal = {ICML}, 
	year = {2006}}
@article{klaus06,
	author={Klaus, Andrea and Sormann, Mario and  Karner, Konrad},
	title={Segment-based Stereo Matching Using Belief Propagation and a Self-adapting Dissimilarity Measure},
	journal={ICPR},
	volume={3},
	pages={15--18},
	year={2006}}	
@article {kleiner14,
    author = {Kleiner, Ariel and Talwalkar, Ameet and Sarkar, Purnamrita and Jordan, Michael I.},
    title = {A Scalable Bootstrap for Massive Data},
    journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
    volume = {76},
    number = {4},
    pages = {795--816},
    keywords = {Bootstrap, Computational efficiency, Estimator quality assessment, Massive data, Resampling},
    year = {2014},
}
@article{kong92,
	author={Kong, Augustine},
	title={A Note on Importance Sampling using Standardized Weights},
	journal={U. of Chicago TR},
	volume={348},
	year={1992}
}		
@article{kucukelbir16,
	author={Kucukelbir, Alp and Tran, Dustin and Ranganath, Rajesh and Gelman, Andrew and Blei, David M.},
	title={Automatic Differentiation Variational Inference},
	journal={arXiv:1603.00788},
	year={2016}}
@article{kullback51,
	author={Kullback, S. and Leibler, R. A.},
	title={On Information and Sufficiency},
	journal={Ann. Math. Stat.},
	volume={22},
	number={1},
	pages={79--86},
	year={1951}
}
@article{kuss05,
	author={Kuss, Malte and Rasmussen, Carl Edward},
	title={Assessing Approximate Inference for Binary Gaussian Process Classification},
	journal={JMLR},
	volume={6},
	pages={1679--1704},
	year={2005}
}
% L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L
@article{li15,
	author={Li, Yingzhen and Hernandez-Lobato, Jose Miguel and Turner, Richard E.},
	title={Stochastic Expectation Propagation},
	journal={NIPS},
	year={2015}}
@article{li16,
	author={Li, Yingzhen and Turner, Richard E.},
	title={Renyi Divergence Variational Inference},
	journal={arXiv:1602.02311},
	year={2016}
}
@article{lienart15,
	title={Expectation Particle Belief Propagation},
	author={Lienart, Thibaut and Teh, Yee Whye and Doucet, Arnaud},
	journal={NIPS},
	year={2015}}

% M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M
@article{mcallister17,
	author={McAllister, Rowan and Gal, Yarin and Kendall, Alex and van der Wilk, Mark and Shah, Amar and Cipolla, Roberto and Weller, Adrian},
	title={Concrete Problems for Autonomous Vehicle Safety: Advantages of Bayesian Deep Learning},
	journal={IJCAI},
	year={2017}
}
@article{miguez13,
	author={Miguez, Joaquin and Koblents Eugenia},
	title={Particle filtering with transformed weights},
	journal={IEEE CAMSAP},
	pages={364--367},
	year={2013}
}
@article{minka01,
	author={Minka, Thomas P.},
	title = {Expectation Propagation for Approximate {Bayesian} Inference},
	year = {2001},
	journal={UAI},
	pages={362--369}}
@article{minka01b,
	author={Minka, Thomas P.},
	title={A Family of Algorithms for Approximate {Bayesian} inference},
	journal={MIT PhD thesis},
	year={2001}}
@article{minka01c,
	title={The EP Energy Function and Minimization Schemes},
	author={Minka, Thomas P.},
	journal={MSR TR},
	year={2001}}
@article{minka04,
	author={Minka, Thomas P.},
	title ={Power {EP}},
	year = {2004},
	journal = {MSR TR}}
@article{minka05,
	author={Minka, Thomas P.},
	title={Divergence Measures and Message Passing},
	journal={MSR TR},
	year={2005}}

@article{moore00,
	author={Moore, Andrew},
	title={The Anchors Hierarchy: Using the Triangle Inequality to Survive High Dimensional Data},
	journal={UAIP},
	year={2000}}
@article{murphy99,
	author={Murphy, Kevin P. and Weiss, Yair and Jordan, Michael I.},
	title={Loopy Belief Propagation for Approximate Inference: an Empirical Study},
	journal={UAI},
	pages={467--475},
	year={1999}}
@article{murray15,
	author={Murray, Lawrence and Lee, Anthony and Jacob, Pierre},
	title={Parallel Resampling in the Particle Filter},
	journal={arXiv:1301.4019v3},
	year={2015}}
@article{murray16,
	author = {Murray, Lawrence and Lee, Anthony and Jacob, Pierre},
	title = {Parallel Resampling in the Particle Filter},
	journal = {JCGS},
	year = {2016}}	
	
% N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N N
@article{nagapetyan17,
	author={Tigran Nagapetyan and Andrew B. Duncan and Leonard Hasenclever and Sebastian J. Vollmer and Lukasz Szpruch and Konstantinos Zygalakis},
	title={The True Cost of Stochastic Gradient Langevin Dynamics},
	journal={arXiv:1706.02692},
	year={2017}}
@article{naesseth15,
	author = {Naesseth, Christian and Lindsten, Fredrik and Sch\"on, Thomas},
	title = {Sequential Monte Carlo for Graphical Models},
	journal = {NIPS},
	year = {2015}}
@book{neal11,
	author={Radford M. Neal},
	title={MCMC using Hamiltonian Dynamics},
	publisher={Chapman \& Hall / CRC Press},
	year={2011},
	pages={113--162}}
@book{nemirovski83,
	author={Nemirovski, Arkadi and Yudin, David},
	publisher = {Wiley},
	title = {Problem Complexity and Method Efficiency in Optimization},
	year = {1983}}
@article{nikolova00,
	author={Nikolova, Mila},
	title={Thresholding Implied by Truncated Quadratic Regularization},
	journal = {IEEE Trans. Sig. Proc.},
	volume = {48},
	number = {12},
	year = {2000},
	pages = {3437-3450}
	}
@article{noorshams13a,
	author = {Noorshams, Nima and Wainwright, Martin J.},
	journal = {JMLR},
	pages = {2799--2835},
	title = {Belief Propagation for Continuous State Spaces: Stochastic Message-Passing with Quantitative Guarantees},
	volume = {14},
	year = {2013}}
@article{noorshams13b,
	author = {Noorshams, Nima and Wainwright, Martin J.},
	journal = {IEEE Trans. Inf. Theory},
	number = {4},
	pages = {1981--2000},
	title = {Stochastic Belief Propagation: A Low-Complexity Alternative to the Sum-Product Algorithm},
	volume = {59},
	year = {2013}}

% O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 
@article{opper98,
	title={A Bayesian Approach to Online Learning},
	author={Opper, Manfred},
	journal={Aston U. TR},
	year={1998}}
@article{opper05,
	title={Expectation Consistent Approximate Inference},
	author={Opper, Manfred and Winther, Ole},
	journal={JMLR},
	volume={1},
	pages={1--48},
	year={2005}}

% P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P
@book{pearl88,
	author = {Pearl, Judea},
	title = {Probabilistic Reasoning in Intelligent Systems},
	publisher = {Morgan Kaufman},
	year = {1988}
	}
@article{peters12,
	author={Peters, E. A. J. F. and de With, G.},
	title={Rejection-free Monte Carlo Sampling for General Potentials},
	journal = {PRE},
	volume = {85},
	issue = {2},
	pages = {026703},
	year = {2012}}

% R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R
@article{ranganath13,
	author={Ranganath, Rajesh and Gerrish, Sean and Blei, David M.},
	title={Black Box Variational Inference},
	journal={arXiv:1401.0118},
	year={2013}}
@article{raskutti15,
	author={Raskutti, Garvesh and Mukherjee, Sayan},
	title={The Information Geometry of Mirror Descent},
	journal={IEEE Trans. Inf. Th. },
	volume={61},
	pages={1451--1457},
	year={2015}}
@book{robert04,
	author={Robert, Christian P. and Casella, George},
	title={Monte Carlo Statistical Methods},
	publisher={Springer},
	year={2004}
}
@book{rockafellar70,
	title={Convex Analysis},
	author={Rockafellar, R. Tyrell},
	publisher={Princeton Mathematical Series},
	year={1970}}
@article{rudin92,
	author={Rudin, Leonid I. and Osher, Stanley and Fatemi, Emad},
	title = {Nonlinear Total Variation Based Noise Removal Algorithms},
	journal = {Physica D},
	volume = {60},
	number = {1},
	pages = {259--268},
	year = {1992}}
% S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S
@article{mnih08,
	title={Bayesian Probabilistic Matrix Factorization using Markov Chain Monte Carlo},
	author={Salakhutdinov, Ruslan and Mnih, Andriy},
	journal={ICML},
	page={880--887},
	year={2008}
}
@article{seeger07,
	title = {Expectation Propagation for Exponential Families},
	author = {Seeger, Matthias},
	journal = {UC Berkeley TR},
	year={2007}}
@article{seeger11,
	title = {Fast Convergence Algorithms for Expectation Propagation Approximate Bayesian Inference}, 
	author = {Seeger, Matthias and Nickish, Hannes},
	journal = {JMLR},
	year={2011}}
@article{sudderth03,
	author = {Sudderth, Erik B. and Ihler, Alexander T. and Freeman, William T. and Willsky, Alan S.},
	journal = {CVPR},
	pages = {605--612},
	title = {Nonparametric Belief Propagation},
	volume = {1},
	year = {2003}}
@inproceedings{sudderth04,
	author = {Sudderth, Erik B. and Mandel, Michael I. and Freeman, William T. and Willsky, Alan S.},
	booktitle = {CVPR},
	title = {Visual Hand tracking Using Nonparametric Belief Propagation},
	year = {2004}}	
@article{sudderth10,
	author = {Sudderth, Erik B. and Ihler, Alexander T. and Isard, Michael and Freeman, William T. and Willsky, Alan S.},
	title={Nonparametric Belief Propagation},
	journal={Commun. ACM},
	volume={53},
	number={10},
	pages={95--102},
	year = {2010}}
@article{sun03,
	author={Sun, Jian and Zheng, Nan-Ning and Shum, Heung-Yeung},
	title={Stereo Matching Using Belief Propagation},
	journal={IEEE Trans. Patt. An. Mach. Int.},
	volume={25},
	pages={787--800},
	year={2003}}

% S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S S
@article{schiff09,
	author={Schiff, Jeremy and Sudderth, Erik B. and Goldberg, Ken},
	title={Nonparametric Belief Propagation for Distributed Tracking of Robot Networks with Noisy Inter-distance Measurements},
	journal={IROS},
	pages={1369-1376},
	year={2009}}
@article{sorensen96,
	author={Sorensen, Danny C.},
	title={Implicitly Restarted Arnoldi/Lanczos Methods for Large Scale Eigenvalue Calculations},
	journal={NASA TR},
	year={1996}
}
@article{srebro04,
	author={Srebro, Nathan and Rennie, Jason D. M. and Jaakkola, Tommi S.},
	title={Maximum-Margin Matrix Factorization},
	journal={NIPS},
	year={2004}
}

% T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T
@article{taghavi12,
	author = {Ehsan Taghavi},
	journal = {Chalmers U. thesis},
	title = {A Study of Linear Complexity Particle Filter Smoothers},
	year = {2012}}
@article{tanizaki01,
	author = {Tanizaki, Hisashi},
	title = {Nonlinear and Non-Gaussian State-Space Modeling with Monte Carlo Techniques: a Survey and Comparative Study},
	journal = {AISM},
	year = {2001}}

% V V V V V V V V V V V V V V V V V V V V V V V V V V V V V V V V V V V V 
@article{vanetti17,
	author={Vanetti, Paul and Bouchard-C\^ot\'e, Alexandre and Deligiannidis, George and Doucet, Arnaud},
	title={Piecewise Deterministic Markov Chain Monte Carlo},
	journal={arXiv:1707.05296},
	year={2017}}

% W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W
@article{wainwright08,
	author = {Wainwright, Martin J. and Jordan, Michael I.},
	title = {Graphical Models, Exponential Families, and Variational Inference},
	journal = {Found. and Tr. in Mach. Learn. },
	volume = {1},
	number = {1--2},
	pages = {1--305},
	year = {2008}}
@article{wang05,
	author={Wang, Bo and Titterington, D. M.},
	title={Inadequacy of Interval Estimates Corresponding to Variational Bayesian Approximations}, 
	journal={AISTATS},
	year={2005}}
@article{weiss00,
	author={Yair Weiss},
	year={2000},
	title={Correctness of Local Probability Propagation in Graphical Models with Loops},
	journal={Neur. Comp. },
	number={1},
	volume={12},
	pages={1--41}}
@article{wu17,
	author={Wu, Changye and Robert, Christian},
	title={Generalized Bouncy Particle Sampler},
	journal={arXiv:1706.04781},
	year={2017}
}

% X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X
@article{xu14,
	title = {Distributed Bayesian Posterior Sampling via Moment Sharing},
	author = {Xu, Minjie and Lakshminarayanan, Balaji and Teh, Yee Whye and Zhu, Jun and Zhang, Bo},
	journal = {NIPS},
	pages = {3356--3364},
	year = {2014}}

% Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y
@article{yedidia00,
	author={Weiss, Yair and Freeman, William T.},
	title={On the Optimality of Solutions of the Max-Product Belief Propagation Algorithm},
	journal={IEEE Trans. Inf. Th. },
	year={2000}
}
@article{yedidia01,
	author={Yedidia, Jonathan S. and Freeman, William T. and Weiss, Yair},
	title={Bethe Free Energy, Kikuchi Approximations and Belief Propagation Algorithms},
	journal={MERL TR},
	year={2001}
}
@article{yedidia02,
	author = {Yedidia, Jonathan S. and Freeman, William T. and Weiss, Yair},
	title = {Constructing Free Energy Approximations and Generalized Belief Propagation Algorithms},
	journal = {MERL TR},
	year = {2002}}
@article{yu06,
	author={Yu, Byron M. and Shenoy, Krishna V. and Sahani, Maneesh},
	journal={NSSP},
	title={Expectation Propagation for Inference in Non-Linear Dynamical
Models with Poisson Observations},
	year={2006}
}
% Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z 
@article{zhao16,
	author = "Zhao, Tianqi and Cheng, Guang and Liu, Han",
	journal = "Ann. Statist.",
	number = "4",
	pages = "1400--1437",
	publisher = "The Institute of Mathematical Statistics",
	title = "A Partially Linear Framework for Massive Heterogeneous Data",
	volume = "44",
	year = "2016"
}
@book{zucchini16,
	author={Zucchini, Walter and MacDonald, Iain L. and Langrock, Roland},
	title={Hidden Markov Models for Time Series: An Introduction Using R},
	publisher={Chapman and Hall},
	year={2016}}