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统计与应用概率论哲学博士

Doctor of Philosophy in Statistics and Applied Probability

加州大学圣塔芭芭拉分校

University of California Santa Barbara

US美国

  • 学历文凭

    学历文凭

    Ph.D.

  • 专业院系

    专业院系

    Department of Statistics and Applied Probability

  • 开学时间

    开学时间

  • 课程时长

    课程时长

  • 课程学费

    课程学费

    汇率提示

国际学生入学条件

The graduate program is open to students who have a Bachelor's Degree in Mathematics, Statistics, or other closely related fields, with an overall minimum GPA of 3.0 or equivelant.
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Requirements
Online application
Statement of Purpose, Personal History and Diversity Statement, and Resume or CV (submitted in the online application)
Three Letters of Recommendation (submitted online)
Official transcripts from all post-secondary institutions attended (submitted online)
English Language Exam Scores (if applicable): TOEFL Internet-Based Test (IBT) total score of 80, or TOEFL Paper-Based Test (PBT) total score of 550, or IELTS Overall Band score of 7
Final/Official transcripts will be required for all applicants who are admitted and have indicated their intent to enroll at UC Santa Barbara by submitting a Statement of Intent to Register (SIR). UC Santa Barbara reserves the right to require official transcripts at any time during the admissions process, and rescind any offer of admission made if discrepancies between uploaded and official transcript(s) are found.
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  • 雅思总分:7
  • 托福网考总分:80
  • 托福笔试总分:550
  • 其他语言考试:Duolingo English Test total score of 120, or higher

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课程简介

我们的统计学和应用概率博士课程为研究生培养了扩展统计学理论和实践领域的界限,以用于现实世界中的问题解决。毕业生接受过学术或行业职业培训,他们致力于新方法和新技术并为之做出贡献。该程序对统计和概率进行严格的数学培训,可用于开发适用于金融,环境科学,计算机科学和生物医学等广泛学科领域的实际方法。最近的论文已写在平滑样条,空间统计,微阵列分析,功能数据模型,经验过程,数学和统计财务,贝叶斯推断和自举估计方法等领域。
he Department of Statistics and Applied Probability offers a Ph.D. program in which students develop a broad understanding of the theory and practice of probability and statistics. The Ph.D. program prepares students to conduct innovative research, beginning with a solid foundation in course work and leading towards original dissertation research. Research topics lie broadly in stochastic modelling, data science and machine learning, with diverse areas, such as financial mathematics, big data analytics, computational methods, artificial intelligence, biostatistics and probabilistic theory underpinning algorithms and applications. Interdisciplinary collaboration areas include computer science, environmental science, physics, mathematics, biological and biomedical sciences, engineering, and finance.
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