Beta-delayed fission probabilities of transfermium nuclei, involved in the r-process

For the nucleosynthesis of heavy and superheavy nuclei fission becomes very important when the r-process runs in a very high neutron density environment. In part, fission is responsible for the formation of heavy nuclei due to the inclusion of fission products as new seed nuclei (fission cycling). More than that, beta-delayed fission, along with spontaneous fission, is responsible in the late stages of the r-process for the suppression of superheavy element yields. For beta-delayed fission probability calculations a model description of the beta-strength- functions is required. Extended theoretical predictions for astro-physical applications were provided long ago, and new predictions also for superheavy nuclei with uptodate nuclear input are needed. For the further extension of data to heavier transactinides the models of strength- functions should be modified, taking into account more complicated level schemes. In our present calculations the strength-function model is based on the quasi-particle approximation of Finite Fermi Systems Theory. The probabilities of beta-delayed fission and beta-delayed neutron emission are calculated for some transfermium neutron-rich nuclei, and the influence of beta-delayed fission upon superheavy element formation is discussed

Classical elliptic hypergeometric functions and their applications

General theory of elliptic hypergeometric series and integrals is outlined. Main attention is paid to the examples obeying properties of the "classical" special functions. In particular, an elliptic analogue of the Gauss hypergeometric function and some of its properties are described. Present review is based on author's habilitation thesis [Spi7] containing a more detailed account of the subject.Comment: 42 pages, typos removed, references update

Bayesian Model Selection for Beta Autoregressive Processes

We deal with Bayesian inference for Beta autoregressive processes. We restrict our attention to the class of conditionally linear processes. These processes are particularly suitable for forecasting purposes, but are difficult to estimate due to the constraints on the parameter space. We provide a full Bayesian approach to the estimation and include the parameter restrictions in the inference problem by a suitable specification of the prior distributions. Moreover in a Bayesian framework parameter estimation and model choice can be solved simultaneously. In particular we suggest a Markov-Chain Monte Carlo (MCMC) procedure based on a Metropolis-Hastings within Gibbs algorithm and solve the model selection problem following a reversible jump MCMC approach