Multivariate extreme events tend to have broader impacts on society compared to individual extremes. Flexible and accurate modeling of multivariate extremes is therefore crucial for many practical applications, including risk assessments and mitigation projects. In this work, we introduce a Bayesian modeling framework for bivariate extremes. Our approach utilizes the geometric extremes framework, for which data are decomposed into angular and radial components. The angular distribution is estimated using periodic M-splines, resulting in a natural circular density, while the radial distribution, conditional on the angles, is approximated using Bezier splines. The radial distribution also provides a means of estimating the limit set, the limiting shape of scaled sample clouds that encodes information pertaining to extremal dependence. Our Bayesian approach offers advantages over competing methods, particularly for uncertainty quantification and interpretation. Furthermore, our framework guarantees theoretical properties of limit sets, ensuring validity for inference. Notably, our model can be used to approximate extreme probabilities associated with failure sets in the joint tail, which are useful across many applications. We demonstrate the efficacy of our approach through numerical experiments and a case study on fire weather in Louisiana, USA, with diagnostics for the latter indicating strong model performance.

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