Bayesian analysis offers a robust framework for deciphering the intricate dynamics of time series data. By treating unknown parameters as random variables, this approach incorporates prior information ...

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 course offers a rigorous yet practical exploration of Bayesian reasoning for data-driven inference and decision-making. Students will gain a deep understanding of probabilistic modeling, and ...

Now over 20 years old, functional MRI (fMRI) has a large and growing literature that is best synthesised with meta-analytic tools. As most authors do not share image data, only the peak activation ...

The core of this patent lies in utilizing large models to handle multimodal data conflicts in chemical HSE scenarios. The technical route can be summarized in several key steps: First, collect ...

Journal of the Royal Statistical Society. Series C (Applied Statistics), Vol. 69, No. 2 (2020), pp. 301-325 (25 pages) The structure of a protein is crucial in determining its functionality and is ...

How do you shape perception ethically when storytelling beats facts? Here is a case for ethical storytelling in our current ...