Mass spectrometry-based lipidomics and metabolomics generate extensive data sets that, along with metadata such as clinical parameters, require specific data exploration skills to identify and ...

It does this by taking listed options data, fitting an arbitrage-free implied volatility surface, and then transforming that fitted object into a probability distribution over future asset prices. In ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Programming is a key transferable skill within the chemical sciences with applications ...

Bayesian probability is a statistical method that applies probability to incorporate prior knowledge or beliefs when making predictions. Unlike traditional probability, which treats each event as ...

Understanding feature distributions is essential in data analysis and preprocessing, as it gives insights into how the data is spread and whether it requires transformations. Here are some ways to ...

Spatial variability and uncertainty associated with soil volumetric moisture content (SVMC) is crucial in moisture prediction accuracy, this paper sets out to address this point of SVMC by developing ...

Data analysis is an integral part of modern data-driven decision-making, encompassing a broad array of techniques and tools to process, visualize, and interpret data. Python, a versatile programming ...

If you use these materials for teaching or research, please use the following citation: Rhoads, S. A. (2023). pyEM: Expectation Maximization with MAP estimation in ...

A new algorithm is suggested based on the central limit theorem for generating pseudo-random numbers with a specified normal or Gaussian probability density function. The suggested algorithm is very ...

The diffusion model is a commonly used tool to infer latent psychological processes underlying decision-making, and to link them to neural mechanisms based on response times. Although efficient open ...