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

US

QS排名:

  • 学历文凭

    Ph.D.

  • 专业院系

    统计

  • 开学时间

  • 课程时长

  • 课程学费

    汇率提示

国际学生入学条件

Our doctoral program in Statistics and Applied Probability is open to those who hold a bachelor's degree in Statistics, Mathematics, or other fields with strong quantitative requirements. Students must have a minimum overall grade point average of 3.0, one year of statistical theory that includes hypothesis testing, confidence intervals, best statistics and most powerful tests, regression and ANOVA concepts, and one course in linear algebra that includes vector spaces, bases in vector spaces, eigenvalues, and eigenvectors.

completed an undergraduate or graduate degree at an institution whose primary language of instruction is English. The minimum score for consideration is 550 when taking the paper-based TOEFL, or 80 when taking the internet-based test, some departments require a higher score. Applicants must make arrangements to take the TOEFL directly with ETS (www.ets.org). Scores should be reported to UCSB using institution code 4835. TOEFL scores must be no more than two years old at the time of application submission. UCSB also considers a minimal score of 7 on the IELTS as an alternative to the TOEFL. IELTS scores must be no more than two years old at the time of application submission
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IDP—雅思考试联合主办方

雅思考试总分

7.0

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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:NA
CRICOS代码:
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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。
Our doctoral program in Statistics and Applied Probability prepares graduate students to expand the boundaries of statistical theory and practice for use in real-world problem solving. Graduates are trained for a career in academics or industry where they are working in and contributing to the forefront of new methods and technology. This program provides rigorous mathematical training in statistics and probability that can be used to develop real-world methodologies applicable to a wide range of interdisciplinary fields including finance, environmental science, computer science, and biomedical science. Recent dissertations have been written in the areas of smoothing splines, spatial statistics, micro-array analysis, functional data models, empirical processes, mathematical and statistical finance, Bayesian inference, and bootstrap estimation methods.
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数据源:泰晤士高等教育世界大学排名

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