A Tutorial on Brownian Motion for Biostatisticians
Fuente:
arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909299526074368 |
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| author | Cui, Elvis Han |
| author_facet | Cui, Elvis Han |
| contents | This manuscript provides an in-depth exploration of Brownian Motion, a fundamental stochastic process in probability theory for Biostatisticians. It begins with foundational definitions and properties, including the construction of Brownian motion and its Markovian characteristics. The document delves into advanced topics such as the Karhunen-Loeve expansion, reflection principles, and Levy's modulus of continuity. Through rigorous proofs and theorems, the manuscript examines the non-differentiability of Brownian paths, the behavior of zero sets, and the significance of local time. The notes also cover important results like Donsker's theorem and Blumenthal's 0-1 law, emphasizing their implications in the study of stochastic processes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_16011 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Tutorial on Brownian Motion for Biostatisticians Cui, Elvis Han Applications Artificial Intelligence Probability Statistics Theory This manuscript provides an in-depth exploration of Brownian Motion, a fundamental stochastic process in probability theory for Biostatisticians. It begins with foundational definitions and properties, including the construction of Brownian motion and its Markovian characteristics. The document delves into advanced topics such as the Karhunen-Loeve expansion, reflection principles, and Levy's modulus of continuity. Through rigorous proofs and theorems, the manuscript examines the non-differentiability of Brownian paths, the behavior of zero sets, and the significance of local time. The notes also cover important results like Donsker's theorem and Blumenthal's 0-1 law, emphasizing their implications in the study of stochastic processes. |
| title | A Tutorial on Brownian Motion for Biostatisticians |
| topic | Applications Artificial Intelligence Probability Statistics Theory |
| url | https://arxiv.org/abs/2408.16011 |