Books on the topic 'Variational Infernce'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 17 books for your research on the topic 'Variational Infernce.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse books on a wide variety of disciplines and organise your bibliography correctly.
Quah, Danny. Exploiting cross section variation for unit root inference in dynamic data. London: London School of Economics, Financial Markets Group, 1994.
Quah, Danny. Exploiting cross section variation for unit root inference in dynamic data. Stockholm: Stockholm University, Institute for International Economic Studies, 1993.
Bartholomew, David J. Statistics without Mathematics. London, UK: SAGE Publications Ltd, 2015.
United States. National Aeronautics and Space Administration., ed. Compositional variation in Apollo 16 impact-melt breccias and inferences for the geology and bombardment history of the central highlands of the moon. [Washington, DC: National Aeronautics and Space Administration, 1994.
United States. National Aeronautics and Space Administration., ed. Compositional variation in Apollo 16 impact-melt breccias and inferences for the geology and bombardment history of the central highlands of the moon. [Washington, DC: National Aeronautics and Space Administration, 1994.
Wainwright, Martin J., and Michael I. Jordan. Graphical Models, Exponential Families, and Variational Inference. Now Publishers, 2008.
Sekhon, Jasjeet. The Neyman— Rubin Model of Causal Inference and Estimation Via Matching Methods. Edited by Janet M. Box-Steffensmeier, Henry E. Brady, and David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0011.
Bortone, Pietro. Language and Nationality: Social Inferences, Cultural Differences, and Linguistic Misconceptions. Bloomsbury Academic & Professional, 2023.
Bortone, Pietro. Language and Nationality: Social Inferences, Cultural Differences, and Linguistic Misconceptions. Bloomsbury Publishing Plc, 2021.
Schadt, Eric E. Network Methods for Elucidating the Complexity of Common Human Diseases. Edited by Dennis S. Charney, Eric J. Nestler, Pamela Sklar, and Joseph D. Buxbaum. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190681425.003.0002.
Burgess, Stephen, and Simon G. Thompson. Mendelian Randomization: Methods for Causal Inference Using Genetic Variants. Taylor & Francis Group, 2021.
Burgess, Stephen, and Simon G. Thompson. Mendelian Randomization: Methods for Causal Inference Using Genetic Variants. Taylor & Francis Group, 2021.
Burgess, Stephen, and Simon G. Thompson. Mendelian Randomization: Methods for Causal Inference Using Genetic Variants. Taylor & Francis Group, 2021.
Chappell, Michael, Bradley MacIntosh, and Thomas Okell. Kinetic Modeling. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198793816.003.0004.
Sprenger, Jan, and Stephan Hartmann. Bayesian Philosophy of Science. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780199672110.001.0001.
Liang, Percy, Michael Jordan, and Dan Klein. Probabilistic grammars and hierarchical Dirichlet processes. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.27.
Volpi, Frédéric. Conclusion. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190642921.003.0007.