Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive derivative trading expertise, Adam is an expert in economics and ...

Bayesian estimation and maximum likelihood methods represent two central paradigms in modern statistical inference. Bayesian estimation incorporates prior beliefs through Bayes’ theorem, updating ...

Bayesian estimation methods form a dynamic branch of statistical inference, utilising Bayes’ theorem to update probabilities in light of new evidence. This framework combines prior knowledge with ...

The Canadian Journal of Statistics / La Revue Canadienne de Statistique, Vol. 46, No. 3 (September/septembre 2018), pp. 399-415 (17 pages) For sparse and high-dimensional data analysis, a valid ...

This is a preview. Log in through your library . Abstract A bivariate distribution with continuous margins can be uniquely decomposed via a copula and its marginal distributions. We consider the ...

We suggest a new method for integrating volatility information for estimating the value-at-risk and conditional value-at-risk of a portfolio. This new method is developed from the perspective of ...

It’s estimated that human adults make about 35,000 decisions a day — the percentage of good decisions depends on the adult. These choices can be as banal as deciding to roll or crumple toilet paper or ...

Mike Lee receives relevant research funding from the Australian Research Council, the Australia-Pacific Science Foundation, and Flinders University. Benedict King receives funding from the Australian ...

Bayesian networks, also known as Bayes nets, belief networks, or decision networks, are a powerful tool for understanding and reasoning about complex systems under uncertainty. They are essentially ...

This course is available on the MSc in Applied Social Data Science, MSc in Data Science, MSc in Econometrics and Mathematical Economics, MSc in Health Data Science, MSc in Quantitative Methods for ...