Articulate the primary interpretations of probability theory and the role these interpretations play in Bayesian inference Use Bayesian inference to solve real-world statistics and data science ...

This paper reviews the general Bayesian approach to parameter estimation in stochastic volatility models with posterior computations performed by Gibbs sampling. The main purpose is to illustrate the ...

Scientists have developed a method to identify symmetries in multi-dimensional data using Bayesian statistical techniques. Bayesian statistics has been in the spotlight in recent years due to ...

Offered through an interdisciplinary partnership, data science at CU Boulder is delivered by the Departments of Applied Mathematics, Computer Science, and Information Science and awarded by the ...

Many antineoplastics are designed to target upregulated genes, but quantifying upregulation in a single patient sample requires an appropriate set of samples for comparison. In cancer, the most ...