Fetching the paper…
Reading the bibliography…
One of the common goals of time series analysis is to use the observed series to inform predictions for future observations.
A predictive approach to model selection
Geisser, S. and Eddy, W. F. (1979) · 1979
Earlier work this paper cites.
Novel approach to nonlinear/non-gaussian bayesian state estimation
Gordon, N. J., Salmond, D. J., and Smith, A. F. (1993) · 1993
Earlier work this paper cites.
Bayesian theory
Bernardo, J. M. and Smith, A. F. (1994) · 1994
Earlier work this paper cites.
Time series analysis
Hamilton, J. D. (1994) · 1994
Earlier work this paper cites.
Optimally combining sampling techniques for monte carlo rendering
Veach, E. and Guibas, L. J. (1995) · 1995
Earlier work this paper cites.
Monte carlo filter and smoother for non-gaussian nonlinear state space models
Kitagawa, G. (1996) · 1996
Earlier work this paper cites.
Simulating ratios of normalizing constants via a simple identity: a theoretical exploration
Meng, X.-L. and Wong, W. H. (1996) · 1996
Earlier work this paper cites.
Bayesian model averaging: a tutorial
Hoeting, J. A., Madigan, D., Raftery, A. E., and Volinsky, C. T. (1999) · 1999
Earlier work this paper cites.
On sequential monte carlo sampling methods for bayesian filtering
Doucet, A., Godsill, S., and Andrieu, C. (2000) · 2000
Earlier work this paper cites.
Introduction to time series and forecasting
Brockwell, P. J., Davis, R. A., and Calder, M. V. (2002) · 2002
Earlier work this paper cites.
Warp bridge sampling
Meng, X.-L. and Schilling, S. (2002) · 2002
Earlier work this paper cites.
Bayesian model assessment and comparison using cross-validation predictive densities
Vehtari, A. and Lampinen, J. (2002) · 2002
Earlier work this paper cites.
Jags: A program for analysis of bayesian graphical models using gibbs sampling
Plummer, M. et al. (2003) · 2003
Cited alongside, same era.
Phenological data series of cherry tree flowering in Kyoto, Japan, and its application to reconstruction of springtime temperatures since the 9th century
Aono, Y. and Kazui, K. (2008) · 2008
Cited alongside, same era.
Predictive likelihood for Bayesian model selection and averaging
Ando, T. and Tsay, R. (2010) · 2010
Cited alongside, same era.
Particle markov chain monte carlo methods
Andrieu, C., Doucet, A., and Holenstein, R. (2010) · 2010
Cited alongside, same era.
Clarifying springtime temperature reconstructions of the medieval period by gap-filling the cherry blossom phenological data series at Kyoto, Japan
Aono, Y. and Saito, S. (2010) · 2010
Cited alongside, same era.
A tutorial on bridge sampling
Gronau, Q. F., Sarafoglou, A., Matzke, D., Ly, A., Boehm, U., Marsman, M., Leslie, D. S., Forster, J. J., Wagenmakers, E.-J., and Steingroever, H. (2017) · 2017
Later among the works it cites.
Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC
Vehtari, A., Gelman, A., and Gabry, J. (2017) · 2017
Later among the works it cites.
tidyverse: Easily Install and Load the ’Tidyverse’
Wickham, H. (2017) · 2017
Later among the works it cites.
Advanced Bayesian multilevel modeling with the R package brms
Bürkner, P.-C. (2018) · 2018
Later among the works it cites.
Automated learning with a probabilistic programming language: Birch
Murray, L. M. and Schön, T. B. (2018) · 2018
Later among the works it cites.
R: A Language and Environment for Statistical Computing
R Core Team (2018) · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A survey of Bayesian predictive methods for model assessment, selection and comparison
Vehtari, A. and Ojanen, J. (2012) · 2012
Cited alongside, same era.
Optimal mixture weights in multiple importance sampling
He, H. Y. and Owen, A. B. (2014) · 2014
Cited alongside, same era.
Hilbert space methods for reduced-rank Gaussian process regression
Solin, A. and Särkkä, S. (2014) · 2014
Cited alongside, same era.
Probabilistic programming in python using PyMC3
Salvatier, J., Wiecki, T. V., and Fonnesbeck, C. (2016) · 2016
Cited alongside, same era.
brms: An R package for Bayesian multilevel models using Stan
Bürkner, P.-C. (2017) · 2017
Cited alongside, same era.
Stan: A probabilistic programming language
Carpenter, B., Gelman, A., Hoffman, M., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M. A., Guo, J., Li, P., and Ridell, A. (2017) · 2017
Cited alongside, same era.
loo: Efficient leave-one-out cross-validation and WAIC for Bayesian models
Vehtari, A., Gabry, J., Yao, Y., and Gelman, A. (2019a)
Cited in the paper.
Using stacking to average Bayesian predictive distributions (with discussion)
Yao, Y., Vehtari, A., Simpson, D., and Gelman, A. (2018) · 2018
Later among the works it cites.
Pyro: Deep universal probabilistic programming
Bingham, E., Chen, J. P., Jankowiak, M., Obermeyer, F., Pradhan, N., Karaletsos, T., Singh, R., Szerlip, P., Horsfall, P., and Goodman, N. D. (2019) · 2019
Closest in time.
Hilbert space methods to approximate Gaussian processes using Stan
Riutort Mayol, G., Andersen, M. R., Bürkner, P., and Vehtari, A. (2019) · 2019
Closest in time.
RStan: the R interface to Stan
Stan Development Team (2019) · 2019
Closest in time.
Efficient leave-one-out cross-validation for bayesian non-factorized normal and student-t models
Bürkner, P.-C., Gabry, J., and Vehtari, A. (2020) · 2020
Closest in time.