Okay, I need to create a Wikipedia-style document in markdown format based on the given categories and keywords. The main topic is "가상 모델" (Virtual Model) under the subcategory of Analysis in Data Science. Let me start by understanding the requirements.

First, the structure: H1 for the title, an overview section, several sections with H2 and H3 headings, and possibly references or related documents. The content needs to be accurate, objective, detailed enough, easy to read, and use technical terms with explanations. Also, markdown formatting with proper hierarchy, lists, tables, emphasis, and code blocks if needed. The document should be at least 800 characters long.

Starting with the title: "가상 모델" as H1. Then an introduction that explains what a virtual model is in the context of data science and analysis. Next sections could include definitions, types, applications, challenges, case studies, and references.

For the overview section, I should define what a virtual model is, maybe mention its purpose in data science. Then under H2, break down into different aspects like definition, types (maybe statistical models, machine learning models, simulation models), applications in various fields, challenges like overfitting or data quality, and case studies.

I need to make sure each section has enough detail. For example, under "정의", explain what a virtual model is, its role in analysis. Under "유형", list different types with brief explanations. Applications could include examples from healthcare, finance, etc. Challenges might involve issues like data scarcity or computational complexity.

I should also check for technical terms and provide simple explanations. For instance, when mentioning machine learning models, maybe briefly explain what they are. Use bullet points or tables where appropriate. Maybe a table comparing different types of virtual models.

Need to ensure the content is over 800 characters. Let me count: each section will have several paragraphs. The introduction might be around 150 words, then each H2 section with a couple of H3 sections. Including examples and case studies would add more length.

Also, check for markdown syntax: proper headers, lists, maybe code blocks if there's an example. But since it's about virtual models in data science, perhaps not too much code. Maybe mention Python libraries like scikit-learn or TensorFlow as examples.

References could include academic papers or books on data science and modeling. Related documents might link to other topics like machine learning or statistical analysis.

Wait, the user specified "가상 모델" which is a bit ambiguous. In Korean, "가상" can mean virtual, simulated, or hypothetical. So I need to clarify that in the context of data science, it refers to models that simulate real-world processes or represent abstract concepts for analysis. Maybe mention both simulation models and conceptual models.

Also, ensure that each section flows logically. Start with definition, then types, applications, challenges, case studies, and conclusion. Use H2 for main sections and H3 for subsections like "1. 통계적 모델" under "유형".

Avoid markdown in the thinking process but structure it properly in the final answer. Make sure to use proper terminology and explain any jargon. Check for clarity and conciseness.