Migrate LaTeX document to Sphinx documentation

[Copilot]

Migrates comprehensive UQ decision tree content from LaTeX to Sphinx markdown format.

Changes

• Introduction: Replaced placeholder with full context on UQ maturation in ML/DL, target audience definitions (4 researcher archetypes), and document navigation guidance

• Decision Tree: Added "The Very First Steps" section with structured assessment framework:

  • Purpose/use-case validation questions
  • Data quality and availability checks
  • Uncertainty type distinctions (aleatoric vs epistemic)
  • Effort evaluation across method complexity, modeling, and computation

• Method Cards (new file): Created dedicated section with evaluation framework

  • 5-dimension rating system (ease of apply, data/task compatibility, integration, cost)
  • MVE/Loss Attenuation as reference implementation with detailed trade-offs
  • Template structure for additional methods

• References: Added recent survey articles (Lopez et al. 2025, Shi et al. 2025, Xu et al. 2025) and original MVE paper (Nix & Weigend 1994)

• Configuration: Updated authors and title to match source document

Mathematical expressions preserved using MyST parser's dollarmath extension:

Mean Variance Estimation (MVE) outputs both $\mu(x)$ and $\sigma^2(x)$, 
modeling the target as a Gaussian distribution.

Documentation builds successfully. The docs/model_cards/ folder remains untouched per requirements.

Original prompt

---

This section details on the original issue you should resolve

<issue_title>migrate the latex document to our sphinx doc</issue_title>
<issue_description>migrate this latex document to sphinx doc. no need to keep the old documents do not touch the docs/model_cards/ folder.

\documentclass{article}
\usepackage{graphicx} % Required for inserting images
\usepackage{multirow}
\usepackage{amsfonts}
\usepackage{hyperref}
\usepackage{amssymb}
\usepackage{stackengine}
\usepackage{scalerel}
\usepackage{xcolor}

\newcommand\openbigstar[1][0.7]{%
  \scalerel*{%
    \stackinset{c}{-.125pt}{c}{}{\scalebox{#1}{\color{white}{$\bigstar$}}}{%
      $\bigstar$}%
  }{\bigstar}
}

% PS: we can change the title later
\title{A decision tree for Uncertainty Quantification}

\author{Moussa Kassem Sbeyti, Peter Steinbach, Alina Bazarova, Athar Khodabakhsh, Leon Tim Engelbert Sievers, <your name>}
\date{January 2026}

\begin{document}

\maketitle

\section{Introduction}

% - UQ ubiqitous in ML and stats literature
Forecasting and parameter estimation lie at the core of modern Machine Learning (ML) systems that support real-world decision-making and policy formation. While substantial progress has been made in improving the predictive performance of data-driven and Deep Learning (DL) models, point estimates alone remain an incomplete representation of model outputs. This has motivated the development of uncertainty quantification (UQ) methods, as well as the adaptation of classical approaches to modern machine-learning architectures, with the goal of characterizing uncertainty arising from data, model assumptions, and limited generalization. In recent years, uncertainty quantification has become a prominent topic within the ML research community, with dedicated workshops on UQ at flagship conferences such as ICML and ICLR, UQ-related main conference talks and significant numbers of accepted papers at NeurIPS, as well as domain-specific survey articles (\cite{pmlr-v287-lopez25a,Shi2025,XU2025115817}).

This increasing focus on uncertainty quantification signals a maturation of machine learning from accuracy-driven prediction toward trustworthy decision support. By explicitly modeling uncertainty, ML systems become more reliable, interpretable, and suitable for deployment in real-world and high-stakes settings.

% - many papers published (Till's account of neurips'25)
% - sign of maturity of ML as a tool -> trustworthiness

\subsection{Motivation}
This work would provide relevant guidance for various ML researchers having different backgrounds and use-cases to take the benefit of a structure to quantify their model/data uncertainties.

\begin{itemize}
    \item Many practitioners have working ML systems
    \item Examples of real life cases when a person potentially might want to explore beyond point estimates
    \item This is our audience who want to explore UQ methods and add them to their project for instance,
    1) Researchers working on tabular data and train ML model on data and want to provide reliable uncertainty estimates for their domain expert collaborators. 2) Researchers working on time-series data and use Neural Networks for deploying on small systems/products. 3) Researchers from domain such as Natural Sciences, who use data science for different data formats such as image, numerical analysis, text, etc., and besides experimental errors and uncertainties require to add the uncertainty from ML tooling. 4) Researcher with quantitative backgrounds who want to use DL and explore suitable paths for including UQ into their inferences.
\end{itemize}


\subsection{How to use this paper}
To guide the reader through this manuscript, we recommend beginning with Section~\nameref{sec:first}, which poses a set of motivating questions and discusses the necessity and practical utility of uncertainty quantification methods across different scenarios. Second, we survey a series of uncertainty quantification methods in Section~\nameref{sec:methods}, with the aim of supporting method selection for particular problem settings.
% - first go to "the very first steps", pick answers\\
% - second select methods from "method cards" alongside the dimensions XYZ\\
% - how the methods are sorted: difficulty, field, datatype mb?\\

\section{The very first steps}\label{sec:first}

While UQ methods are potentially powerful, their effectiveness and suitability for a particular use case are highly dependent on the model setup and the intended goals of the study. In this section, we present a series of guiding questions along with their explanations, intended to help readers assess in advance whether UQ is likely to benefit their research. As a general guidance we note, that it is important to collect all available prior knowledge about the problem and clearly define the expected role and objectives of uncertainty quantification.

%\subsubsection {\textbf{Purpose/Use-cases}}
\paragraph{Is Uncertainty Quantification necessary, and if s...

</details>



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