an out-of-sample test set of size $500$ simulated from $p(\mathbf{x})$. A value of EDM close to zero indicates that the fitted model reconstructs the DGP very well.

### Performance assessment of PSLiB

To assess the effect of censoring and the covariate dimension on the performance of PSLiB, datasets are simulated from different DGPs: the number of covariates $P=1,5,10$, and the proportion of censored observations $p_{c}=10\%,25\%,50\%$ (for each $P$). That is, in total there are nine DGPs, and for each DGP, $50$ datasets of size $n=2500$ and survival time length $J=26$ are simulated. Figure \[Effect of censoring and size of the covariate\] diplays the boxplots of the EDM between the DGP models and their corresponding fitted models. Models are fitted using PSLiB with $K=2000$ particles. Experiments show that increasing $K$ does not improve the EDM values. Furthermore, a discount factor $0.3\leq\phi\leq0.45$ yields relatively low EDM values with the lowest obtained in most cases when $\phi=0.45$ (results not shown). Therefore, in the following analyses $\phi$ is set to the latter value.

![[Evaluating the performance of PSLiB with respect to the dimension of the covariate vector and the proportion of censored observations. Results are based on $50$ simulated datasets of size $n=2500$ and 