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<p>Create Page</p> <p><b>Nouvelle page</b></p><div>== Overview ==<br /> <br /> &#039;&#039;&#039;Ash Twin&#039;&#039;&#039; is an intern project designed to streamline Bayesian data analysis for time series data.<br /> <br /> It is implemented as a &#039;&#039;&#039;[[Python]] pipeline&#039;&#039;&#039;, providing a sequence of operations to process and analyze time series data using Bayesian methods. The project is under development, and a paper detailing its functionality is planned for future publication.<br /> <br /> == Key Concepts ==<br /> <br /> * &#039;&#039;&#039;Bayesian data analysis:&#039;&#039;&#039; A statistical approach that incorporates prior knowledge along with observed data to make inferences.<br /> * &#039;&#039;&#039;Time series:&#039;&#039;&#039; Data points recorded sequentially over time, often used in experiments or longitudinal studies.<br /> * &#039;&#039;&#039;[[Python]] pipeline:&#039;&#039;&#039; A structured sequence of computational steps implemented in [[Python]].<br /> <br /> == Main Uses ==<br /> <br /> * Process and analyze experimental time series data<br /> * Apply Bayesian statistical methods to derive insights from measurements<br /> * Provide a structured workflow for data analysis using [[Python]]<br /> <br /> == Status ==<br /> <br /> * Project is currently under development<br /> * A scientific paper describing its methodology and applications is forthcoming</div>

Pierre.demangel