Читать книгу Computational Statistics in Data Science - Группа авторов - Страница 18

These challenges will remain throughout the twenty‐first century, but it is possible to make significant advances for specific statistical tasks or classes of models. Section 3.1 considers Bayesian sparse regression based on continuous shrinkage priors, designed to alleviate the heavy multimodality (big ) of the more traditional spike‐and‐slab approach. This model presents a major computational challenge as and grow, but a recent computational advance makes the posterior inference feasible for many modern large‐scale applications.

And because of the rise of data science, there are increasing opportunities for computational statistics to grow by enabling and extending statistical inference for scientific applications previously outside of mainstream statistics. Here, the science may dictate the development of structured models with complexity possibly growing in and . Section 3.2 presents a method for fast phylogenetic inference, where the primary structure of interest is a “family tree” describing a biological evolutionary history.