In Sweden, reporting is voluntary for most common felled game, and the number of voluntary reports can vary between hunting teams, HMP, and counties. In 2020, an improved harvest estimation model was developed, which reduced the sensitivity to low reporting. However, there were still some limits to the model, where large, credible intervals were estimated. Therefore, additional variables were considered as the model does not take into account landcover among HMPs, [2] the impact of climate, [4] wildlife accidents, and [4] geographical distribution, creating the covariate model. This study aimed to compare the new model with the covariate model to see if covariates would reduce the large, credible intervals. Two hypothesis tests were performed: evaluation of predictive performance using leave one out cross-validation and evaluation of the 95 % credible interval. Evaluation of predictive performance was performed by examining the difference in expected log-pointwise predictive density (ELPD) and standard error (SE) for each species and model. The results show that the covariates model ranked highest for all ten species, and out of the ten species, six had an (ELPD) difference of two to four, which implies that there is support that the covariate model will be a better predictor for other datasets than this one. At least one covariate had an apparent effect on harvest estimates for nine out of ten species. Finally, the covariate model reduced the large uncertainties, which was an improvement of the null model, indicating that harvest estimates can be improved by taking covariates into account.

Ce mémoire porte sur la simulation d'intervalles de crédibilité simultanés dans un contexte bayésien. Dans un premier temps, nous nous intéresserons à des données de précipitations et des fonctions basées sur ces données : la fonction de répartition empirique et la période de retour, une fonction non linéaire de la fonction de répartition. Nous exposerons différentes méthodes déjà connues pour obtenir des intervalles de confiance simultanés sur ces fonctions à l'aide d'une base polynomiale et nous présenterons une méthode de simulation d'intervalles de crédibilité simultanés. Nous nous placerons ensuite dans un contexte bayésien en explorant différents modèles de densité a priori. Pour le modèle le plus complexe, nous aurons besoin d'utiliser la simulation Monte-Carlo pour obtenir les intervalles de crédibilité simultanés a posteriori. Finalement, nous utiliserons une base non linéaire faisant appel à la transformation angulaire et aux splines monotones pour obtenir un intervalle de crédibilité simultané valide pour la période de retour. / This master's thesis addresses the problem of the simulation of simultaneous credible intervals in a Bayesian context. First, we will study precipation data and two functions based on these data : the empirical distribution function and the return period, a non-linear function of the empirical distribution. We will review different methods already known to obtain simultaneous confidence intervals of these functions with a polynomial basis and we will present a method to simulate simultaneous credible intervals. Second, we will explore some models of prior distributions and in the more complex one, we will need the Monte-Carlo method to simulate simultaneous posterior credible intervals. Finally, we will use a non-linear basis based on the angular transformation and on monotone splines to obtain valid simultaneous credible intervals for the return period.