Markov chain Monte Carlo (MCMC)-based simulation approaches are by far the most common method in Bayesian inference to access the posterior distribution. Recently, motivated by successes in machine learning, variational inference (VI) has gained in interest in statistics since it promises a...

Many papers in the litterature have adopted the expected utility paradigm to analyze insurancedecisions. Insurance companies manage policies by growing, by adding independent risks.Even if adding risks generally ultimately decreases the probability of insolvency, the impacton the insurer's...

In this paper, we consider a décision-maker facing a financial risk flanked by a backgroundrisk, possibly non-financial, such as health or environmental risk. A decision has to be madeabout the amount of an investment (in the financial dimension) resulting in a future benefiteither in the same...

This paper delevops a tools to analyse the ordering of concordance of random vectors.

This papfer deals with distributional free inference to test for positive quadrant dependence, i.e. for the probability that two variables are simultaneously small (or large) being at least as great as it would be were they dependent.

In this paper, we propose an analytic analogue to the simulation procedure described in Taylor (1997). We apply the formulas to a Belgian data set and discuss the interaction between a priori and a posteriori ratemakings.

This paper aims to study the kind of dependence induced by the introduction of correlated latent variables in the annual numbers of claims reported by policyholders.

In this paper, it is shown how to approximate theoretical premiumcalculation principles in order to make them useful in practice...

Mortality projections are major concerns for public policy, social security and private insurance. This paper implements a Bayesian log-bilinear Poisson regression model to forecast mortality. Computations are carried out using Markov Chain Monte Carlo methods in which the degree of smoothing is...