update PSIS references in vignettes

vignettes/loo2-example.Rmd

@@ -28,9 +28,12 @@ In this vignette we can't provide all necessary background information on
 PSIS-LOO and its diagnostics (Pareto $k$ and effective sample size), so we
 encourage readers to refer to the following papers for more details:
 
-* Vehtari, A., Gelman, A., and Gabry, J. (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. _Statistics and Computing_. 27(5), 1413--1432. \doi:10.1007/s11222-016-9696-4. Links: [published](https://link.springer.com/article/10.1007/s11222-016-9696-4) | [arXiv preprint](https://arxiv.org/abs/1507.04544).
+* Vehtari, A., Gelman, A., and Gabry, J. (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. _Statistics and Computing_. 27(5), 1413--1432. \doi:10.1007/s11222-016-9696-4. Links: [published](https://link.springer.com/article/10.1007/s11222-016-9696-4) | [preprint arXiv](https://arxiv.org/abs/1507.04544).
 
-* Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2022). Pareto smoothed importance sampling. [arXiv preprint arXiv:1507.02646](https://arxiv.org/abs/1507.02646).
+* Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2024).
+Pareto smoothed importance sampling.  *Journal of Machine Learning Research*,
+accepted for publication.
+[arXiv preprint arXiv:1507.02646](https://arxiv.org/abs/1507.02646)
 
 
 # Setup
@@ -145,7 +148,7 @@ bad. Since we have some $k>1$, we are not able to compute an estimate for the
 Monte Carlo standard error (SE) of the expected log predictive density
 (`elpd_loo`) and `NA` is displayed. (Full details on the interpretation of 
 the Pareto $k$ diagnostics are available in the Vehtari, Gelman, and Gabry
-(2017) and Vehtari, Simpson, Gelman, Yao, and Gabry (2019) papers referenced
+(2017) and Vehtari, Simpson, Gelman, Yao, and Gabry (2024) papers referenced
 at the top of this vignette.)
 
 In this case the `elpd_loo` estimate should not be considered reliable. If we
@@ -297,4 +300,7 @@ Computing_. 27(5), 1413--1432. \doi:10.1007/s11222-016-9696-4.
 [online](https://link.springer.com/article/10.1007/s11222-016-9696-4), 
 [arXiv preprint arXiv:1507.04544](https://arxiv.org/abs/1507.04544).
 
-Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2019). Pareto smoothed importance sampling. [arXiv preprint arXiv:1507.02646](https://arxiv.org/abs/1507.02646).
+Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2024).
+Pareto smoothed importance sampling.  *Journal of Machine Learning Research*,
+accepted for publication.
+[arXiv preprint arXiv:1507.02646](https://arxiv.org/abs/1507.02646)

vignettes/loo2-large-data.Rmd

@@ -35,7 +35,10 @@ Proceedings of the 23rd International Conference on Artificial Intelligence and
 
 * Vehtari, A., Gelman, A., and Gabry, J. (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. _Statistics and Computing_. 27(5), 1413--1432. \doi:10.1007/s11222-016-9696-4. Links: [published](https://link.springer.com/article/10.1007/s11222-016-9696-4) | [arXiv preprint](https://arxiv.org/abs/1507.04544).
 
-* Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2022). Pareto smoothed importance sampling. [arXiv preprint arXiv:1507.04544](https://arxiv.org/abs/1507.04544).
+* Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2024).
+Pareto smoothed importance sampling.  *Journal of Machine Learning Research*,
+accepted for publication.
+[arXiv preprint arXiv:1507.02646](https://arxiv.org/abs/1507.02646)
 
 which provide important background for understanding the methods implemented in
 the package.
@@ -608,6 +611,7 @@ Computing_. 27(5), 1413--1432. \doi:10.1007/s11222-016-9696-4.
 [online](https://link.springer.com/article/10.1007/s11222-016-9696-4), 
 [arXiv preprint arXiv:1507.04544](https://arxiv.org/abs/1507.04544).
 
-Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2022). Pareto
-smoothed importance sampling.
-[arXiv preprint arXiv:1507.02646](https://arxiv.org/abs/1507.02646).
+Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2024).
+Pareto smoothed importance sampling.  *Journal of Machine Learning Research*,
+accepted for publication.
+[arXiv preprint arXiv:1507.02646](https://arxiv.org/abs/1507.02646)

vignettes/loo2-lfo.Rmd

@@ -54,7 +54,7 @@ leave-one-out cross-validation (LOO-CV). For a data set with $N$ observations,
 we refit the model $N$ times, each time leaving out one of the $N$ observations
 and assessing how well the model predicts the left-out observation. LOO-CV is
 very expensive computationally in most realistic settings, but the Pareto
-smoothed importance sampling (PSIS, Vehtari et al, 2017, 2022) algorithm provided by
+smoothed importance sampling (PSIS, Vehtari et al, 2017, 2024) algorithm provided by
 the *loo* package allows for approximating exact LOO-CV with PSIS-LOO-CV.
 PSIS-LOO-CV requires only a single fit of the full model and comes with
 diagnostics for assessing the validity of the approximation.
@@ -179,7 +179,7 @@ variability of the importance ratios $r_i^{(s)}$ will become too large and
 importance sampling will fail. We will refer to this particular value of $i$ as
 $i^\star_1$. To identify the value of $i^\star_1$, we check for which value of
 $i$ does the estimated shape parameter $k$ of the generalized Pareto
-distribution first cross a certain threshold $\tau$ (Vehtari et al, 2022). Only
+distribution first cross a certain threshold $\tau$ (Vehtari et al, 2024). Only
 then do we refit the model using the observations up to $i^\star_1$ and restart
 the process from there by setting $\theta^{(s)} = \theta^{(s)}_{1:i^\star_1}$
 and $i^\star = i^\star_1$ until the next refit.
@@ -188,7 +188,7 @@ In some cases we may only need to refit once and in other cases we will find a
 value $i^\star_2$ that requires a second refitting, maybe an $i^\star_3$ that
 requires a third refitting, and so on. We refit as many times as is required
 (only when $k > \tau$) until we arrive at observation $i = N - M$. 
-For LOO, assuming posterior sample size is 4000 or larger, we recommend to use a threshold of $\tau = 0.7$ (Vehtari et al, 2017, 2022)
+For LOO, assuming posterior sample size is 4000 or larger, we recommend to use a threshold of $\tau = 0.7$ (Vehtari et al, 2017, 2024)
 and it turns out this is a reasonable threshold for LFO as well (Bürkner et al. 2020).
 
 ## Autoregressive models
@@ -640,7 +640,10 @@ Bürkner P. C., Gabry J., & Vehtari A. (2020). Approximate leave-future-out cros
 
 Vehtari A., Gelman A., & Gabry J. (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. *Statistics and Computing*, 27(5), 1413--1432. \doi:10.1007/s11222-016-9696-4. [Online](https://link.springer.com/article/10.1007/s11222-016-9696-4). [arXiv preprint arXiv:1507.04544](https://arxiv.org/abs/1507.04544).
 
-Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2022). Pareto smoothed importance sampling. [arXiv preprint arXiv:1507.02646](https://arxiv.org/abs/1507.02646).
+Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2024).
+Pareto smoothed importance sampling.  *Journal of Machine Learning Research*,
+accepted for publication.
+[arXiv preprint arXiv:1507.02646](https://arxiv.org/abs/1507.02646)
 
 <br />
 

vignettes/loo2-mixis.Rmd

@@ -195,7 +195,10 @@ Silva L. and Zanella G. (2022). Robust leave-one-out cross-validation for high-d
 
 Vehtari A., Gelman A., and Gabry J. (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. *Statistics and Computing*, 27(5), 1413--1432. Preprint at [arXiv:1507.04544](https://arxiv.org/abs/1507.04544)
 
-Vehtari A., Simpson D., Gelman A., Yao Y., and Gabry J. (2022). Pareto smoothed importance sampling. Preprint at [arXiv:1507.02646](https://arxiv.org/abs/1507.02646)
+Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2024).
+Pareto smoothed importance sampling.  *Journal of Machine Learning Research*,
+accepted for publication.
+[arXiv preprint arXiv:1507.02646](https://arxiv.org/abs/1507.02646)
 
 
 

vignettes/loo2-moment-matching.Rmd

@@ -43,9 +43,10 @@ papers
 
 * Vehtari, A., Gelman, A., and Gabry, J. (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. _Statistics and Computing_. 27(5), 1413--1432. \doi:10.1007/s11222-016-9696-4. Links: [published](https://link.springer.com/article/10.1007/s11222-016-9696-4) | [arXiv preprint](https://arxiv.org/abs/1507.04544).
 
-* Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2022). 
-Pareto smoothed importance sampling. 
-[arXiv preprint arXiv:1507.02646](https://arxiv.org/abs/1507.02646).
+* Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2024).
+Pareto smoothed importance sampling.  *Journal of Machine Learning Research*,
+accepted for publication.
+[arXiv preprint arXiv:1507.02646](https://arxiv.org/abs/1507.02646)
 
 # Example: Eradication of Roaches
 
@@ -319,4 +320,7 @@ Implicitly adaptive importance sampling. _Statistics and Computing_, 31, 16.
 
 Vehtari, A., Gelman, A., and Gabry, J. (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. _Statistics and Computing_. 27(5), 1413--1432. \doi:10.1007/s11222-016-9696-4. Links: [published](https://link.springer.com/article/10.1007/s11222-016-9696-4) | [arXiv preprint](https://arxiv.org/abs/1507.04544).
 
-Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2022). Pareto smoothed importance sampling. [arXiv preprint arXiv:1507.02646](https://arxiv.org/abs/1507.02646).
+Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2024).
+Pareto smoothed importance sampling.  *Journal of Machine Learning Research*,
+accepted for publication.
+[arXiv preprint arXiv:1507.02646](https://arxiv.org/abs/1507.02646)

vignettes/loo2-non-factorized.Rmd

@@ -183,7 +183,7 @@ referred to as PSIS-LOO (Vehtari et al, 2017).
 
 In order to validate the approximate LOO procedure, and also in order to allow
 exact computations to be made for a small number of leave-one-out folds for
-which the Pareto $k$ diagnostic (Vehtari et al, 2022) indicates an unstable
+which the Pareto $k$ diagnostic (Vehtari et al, 2024) indicates an unstable
 approximation, we need to consider how we might to do _exact_ leave-one-out CV
 for a non-factorized model. In the case of a Gaussian process that has the
 marginalization property, we could just drop the one row and column of $C$
@@ -417,7 +417,7 @@ psis_result <- psis(log_ratios)
 
 The quality of the PSIS-LOO approximation can be investigated graphically by
 plotting the Pareto-k estimate for each observation. The approximation is robust up to values
-of $0.7$ (Vehtari et al, 2017, 2022). In the plot below, we see that the fourth
+of $0.7$ (Vehtari et al, 2017, 2024). In the plot below, we see that the fourth
 observation is problematic and so may reduce the accuracy of the LOO-CV
 approximation.
 
@@ -716,4 +716,7 @@ Vehtari A., Mononen T., Tolvanen V., Sivula T., & Winther O. (2016). Bayesian le
 
 Vehtari A., Gelman A., & Gabry J. (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. *Statistics and Computing*, 27(5), 1413--1432. \doi:10.1007/s11222-016-9696-4. [Online](https://link.springer.com/article/10.1007/s11222-016-9696-4). [arXiv preprint arXiv:1507.04544](https://arxiv.org/abs/1507.04544).
 
-Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2022). Pareto smoothed importance sampling. [arXiv preprint arXiv:1507.02646](https://arxiv.org/abs/1507.02646).
+Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2024).
+Pareto smoothed importance sampling.  *Journal of Machine Learning Research*,
+accepted for publication.
+[arXiv preprint arXiv:1507.02646](https://arxiv.org/abs/1507.02646)

vignettes/loo2-weights.Rmd

@@ -366,7 +366,10 @@ Computing_. 27(5), 1413--1432. \doi:10.1007/s11222-016-9696-4.
 [online](https://link.springer.com/article/10.1007/s11222-016-9696-4), 
 [arXiv preprint arXiv:1507.04544](https://arxiv.org/abs/1507.04544).
 
-Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2022). Pareto smoothed importance sampling. [arXiv preprint arXiv:1507.02646](https://arxiv.org/abs/1507.02646).
+Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2024).
+Pareto smoothed importance sampling.  *Journal of Machine Learning Research*,
+accepted for publication.
+[arXiv preprint arXiv:1507.02646](https://arxiv.org/abs/1507.02646)
 
 Yao, Y., Vehtari, A., Simpson, D., and Gelman, A. (2018). Using
 stacking to average Bayesian predictive distributions. In Bayesian

vignettes/loo2-with-rstan.Rmd

@@ -29,7 +29,10 @@ Some sections from this vignette are excerpted from our papers
 
 * Vehtari, A., Gelman, A., and Gabry, J. (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. _Statistics and Computing_. 27(5), 1413--1432. \doi:10.1007/s11222-016-9696-4. Links: [published](https://link.springer.com/article/10.1007/s11222-016-9696-4) | [arXiv preprint](https://arxiv.org/abs/1507.04544).
 
-* Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2022). Pareto smoothed importance sampling. [arXiv preprint arXiv:1507.04544](https://arxiv.org/abs/1507.02646).
+* Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2024).
+Pareto smoothed importance sampling.  *Journal of Machine Learning Research*,
+accepted for publication.
+[arXiv preprint arXiv:1507.02646](https://arxiv.org/abs/1507.02646)
 
 which provide important background for understanding the methods implemented in
 the package.
@@ -234,6 +237,7 @@ Computing_. 27(5), 1413--1432. \doi:10.1007/s11222-016-9696-4.
 [online](https://link.springer.com/article/10.1007/s11222-016-9696-4), 
 [arXiv preprint arXiv:1507.04544](https://arxiv.org/abs/1507.04544).
 
-Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2022). Pareto
-smoothed importance sampling.
-[arXiv preprint arXiv:1507.02646](https://arxiv.org/abs/1507.02646).
+Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2024).
+Pareto smoothed importance sampling.  *Journal of Machine Learning Research*,
+accepted for publication.
+[arXiv preprint arXiv:1507.02646](https://arxiv.org/abs/1507.02646)