📖 Start here: 5-minute introduction page — hero, install, five runnable snippets covering simulation → fitting → KS goodness-of-fit → decoding → SSGLM, plus a paper-example thumbnail gallery and v1.4 ...
This library supports calculation of uniform boundaries, confidence sequences, and always-valid p-values. These constructs are useful in sequential A/B testing, best-arm identification, and other ...
The Gaussian copula is a pivotal tool in credit risk modelling, widely used for assessing the joint probability of credit events, such as defaults, in portfolios like collateralised debt obligations ...
Modern, large-scale scientific datasets with tens of thousands of variables and millions of samples can accelerate scientific discovery by revealing underlying structure in data based on dependence ...
In the realm of probability and statistics, the binomial distribution serves as a cornerstone concept, shaping decisions across industries. It is essential for understanding scenarios with a fixed ...
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 ...
While sub-clustering cell-populations has become popular in single cell-omics, negative controls for this process are lacking. Popular feature-selection/clustering ...
Neuromorphology is crucial to identifying neuronal subtypes and understanding learning. It is also implicated in neurological disease. However, standard morphological analysis focuses on macroscopic ...
Bayesian statistics is an approach to data analysis based on Bayes’ theorem, where available knowledge about parameters in a statistical model is updated with the information in observed data. The ...
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