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ionides · Nov 5, 2023 · e876de6 · e876de6
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   \item Ning, N. and Ionides, E. L. (2023). Iterated block particle filter for high-dimensional parameter learning: Beating the curse of dimensionality. {\it Journal of Machine Learning Research} {\volume 24}{\separator}1--76.
 
+    \item  Ionides, E. L., Asfaw, K., Park, J., and King, A. A. (2023). Bagged filters for partially observed interacting systems. {\em Journal of the American Statistical Association} {\volume 118}{\separator}1078-1089. 
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   \item Ionides, E. L., Ning, N. and Wheeler, J. (2022). An iterated block particle filter for inference on coupled dynamic systems with shared and unit-specific parameters. {\it Statistica Sinica}, pre-published online.
 
 \item Ionides, E. L. and Ritov, Y. (2022). The scientific method and p-values: Response to Mayo (2022). {\em Conservation Biology} {\volume 36}{\separator}e13984.
 
 \item  King, A. A., Lin, Q., and Ionides, E. L. (2022). Markov genealogy processes. {\em Theoretical Population Biology} {\volume 143}{\separator}77--91.
 
-\item  Ionides, E. L., Asfaw, K., Park, J., and King, A. A. (2021). Bagged filters for partially observed interacting systems. {\em Journal of the American Statistical Association}, pre-published online. 
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 \item Ning, N., Ionides, E. L., and Ritov, Y. (2021). Scalable Monte Carlo inference and rescaled local asymptotic normality. {\em Bernoulli}, {\volume 27}{\separator}2532--2555.
 
   \item Park, J., and Ionides, E. L. (2020). Inference on high-dimensional implicit dynamic models using a guided intermediate resampling filter. {\em Statistics and Computing}, {\volume 30}{\separator}1497–-1522.