In this tutorial, we explore ModelScope through a practical, end-to-end workflow that runs smoothly on Colab. We begin by setting up the environment, verifying dependencies, and confirming GPU ...

In this tutorial, we build an end-to-end visual document retrieval pipeline using ColPali. We focus on making the setup robust by resolving common dependency conflicts and ensuring the environment ...

Abstract: This tutorial introduces a new cuisine to the simulationist kitchen: plausible inference, the culinary art of output analysis that concocts statistical inferences about possibly unsimulated ...

The Bayesian approach to statistical inference and other data analysis tasks gets its name from Bayes’s theorem (BT). BT specifies that a posterior probability for a hypothesis concerning a data ...

Diffusion magnetic resonance imaging (dMRI) is a versatile imaging technique that has gained popularity thanks to its sensitive ability to measure displacement of water molecules within a living ...

Inferring parameters of computational models that capture experimental data is a central task in cognitive neuroscience. Bayesian statistical inference methods usually require the ability to evaluate ...

The analysis of spatial point patterns has greatly advanced our understanding of ecological processes. However, the methods currently available for analyzing replicated spatial point patterns (RSPPs) ...

Choosing a statistical model and accounting for uncertainty about this choice are important parts of the scientific process and are required for common statistical tasks such as parameter estimation, ...

The MSqRob package allows a user to do quantitative protein-level statistical inference on LC-MS proteomics data. More specifically, our package makes use of peptide-level input data, thus correcting ...