Every day, weather forecasting centres around the world make use of noisy, incomplete observations atmosphere to update their forecasts. This process is known as data assimilation, fusion or state estimation and best expressed Bayesian inference: given a set observations, some prior beliefs model target system, what probability distribution unobserved quantities latent variables at time,...

Markov chain theory plays an important role in statistical inference both in the formulation of models for data and in the construction of efficient algorithms for inference. The use of Markov chains in modeling data has a long history, however the use of Markov chain theory in developing algorithms for statistical inference has only become popular recently. Using mark-recapture models as an il...

background : to prepare safe and adequate blood supply to meet patients’ needs and ensure a sufficient number of regular blood donors, knowledge about factors encouraging people to donate blood regularly is essential. considering its importance, we aimed to identifying the effective factors of the return to blood donation in based on zero-inflated count regression models using bayesian approach...