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    • Bayesian Statistics

    筛选依据

    ''bayesian statistics'的 68 个结果

    • University of California, Santa Cruz

      University of California, Santa Cruz

      Bayesian Statistics

      您将获得的技能: Bayesian, Bayesian Statistics, Econometrics, Forecasting, General Statistics, Graph Theory, Machine Learning, Markov Model, Mathematics, Probability & Statistics, Probability Distribution, R Programming, Regression, Statistical Machine Learning, Statistical Programming, Theoretical Computer Science

      4.6

      (3.2k 条评论)

      Intermediate · Specialization · 3+ Months

    • University of California, Santa Cruz

      University of California, Santa Cruz

      Bayesian Statistics: From Concept to Data Analysis

      您将获得的技能: General Statistics, Statistical Programming, Bayesian Statistics, Probability & Statistics, R Programming, Probability, Bayesian, Probability Distribution, Inference

      4.6

      (2.9k 条评论)

      Intermediate · Course · 1-4 Weeks

    • Duke University

      Duke University

      Bayesian Statistics

      您将获得的技能: Bayesian, Probability & Statistics, General Statistics, Probability Distribution, Bayesian Statistics

      3.8

      (771 条评论)

      Intermediate · Course · 1-3 Months

    • 免费

      University of Zurich

      University of Zurich

      An Intuitive Introduction to Probability

      您将获得的技能: Bayesian Network, Data Analysis, Machine Learning, Bayesian Statistics, General Statistics, Probability, Studentized Residual, Chi-Squared Distribution, Probability & Statistics, Probability Distribution, Basic Descriptive Statistics

      4.8

      (1.3k 条评论)

      Beginner · Course · 1-3 Months

    • Databricks

      Databricks

      Introduction to Bayesian Statistics

      您将获得的技能: Bayesian Statistics, Probability Distribution, Probability & Statistics, General Statistics

      3.3

      (21 条评论)

      Beginner · Course · 1-4 Weeks

    • 免费

      Duke University

      Duke University

      Data Science Math Skills

      您将获得的技能: Algebra, General Statistics, Mathematics, Bayesian Statistics, Computational Logic, Graph Theory, Probability Distribution, Mathematical Theory & Analysis, Probability & Statistics, Factorial, Probability, Theoretical Computer Science

      4.5

      (10.2k 条评论)

      Beginner · Course · 1-3 Months

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      免费

      Eindhoven University of Technology

      Eindhoven University of Technology

      Improving your statistical inferences

      您将获得的技能: Bayesian Network, Statistical Inference, Machine Learning, Bayesian Statistics, Probability & Statistics, Inference, Experiment, Statistical Tests, Interpretation, General Statistics, Bayesian

      4.9

      (715 条评论)

      Intermediate · Course · 1-3 Months

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      University of California, Santa Cruz

      University of California, Santa Cruz

      Bayesian Statistics: Techniques and Models

      您将获得的技能: Mathematics, Machine Learning, Statistical Programming, Econometrics, Bayesian Statistics, Regression, Markov Model, Probability & Statistics, General Statistics, Bayesian, Statistical Machine Learning, Graph Theory, Probability Distribution

      4.8

      (438 条评论)

      Intermediate · Course · 1-3 Months

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      University of California, Santa Cruz

      University of California, Santa Cruz

      Bayesian Statistics: Mixture Models

      您将获得的技能: Machine Learning, Bayesian Statistics, Theoretical Computer Science, Statistical Machine Learning, General Statistics, Probability & Statistics

      4.6

      (28 条评论)

      Intermediate · Course · 1-3 Months

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      University of California, Santa Cruz

      University of California, Santa Cruz

      Bayesian Statistics: Time Series Analysis

      您将获得的技能: Forecasting, General Statistics, Probability & Statistics

      Intermediate · Course · 1-3 Months

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      University of California, Santa Cruz

      University of California, Santa Cruz

      Bayesian Statistics: Capstone Project

      Advanced · Course · 1-4 Weeks

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      University of Michigan

      University of Michigan

      Statistics with Python

      您将获得的技能: Bayesian Statistics, Business Analysis, Communication, Computer Programming, Confidence, Data Analysis, Data Visualization, Econometrics, Experiment, General Statistics, Hypothesis, Inference, Machine Learning, Machine Learning Algorithms, Marketing, Modeling, Probability & Statistics, Programming Principles, Python Programming, Regression, Statistical Analysis, Statistical Hypothesis Testing, Statistical Inference, Statistical Programming, Statistical Tests, Statistical Visualization

      4.6

      (2.7k 条评论)

      Beginner · Specialization · 1-3 Months

    与 bayesian statistics 相关的搜索

    bayesian statistics: techniques and models
    bayesian statistics: time series analysis
    bayesian statistics: from concept to data analysis
    bayesian statistics: mixture models
    bayesian statistics: capstone project
    introduction to bayesian statistics
    1234…6

    总之,这是我们最受欢迎的 bayesian statistics 门课程中的 10 门

    • Bayesian Statistics: University of California, Santa Cruz
    • Bayesian Statistics: From Concept to Data Analysis: University of California, Santa Cruz
    • Bayesian Statistics: Duke University
    • An Intuitive Introduction to Probability: University of Zurich
    • Introduction to Bayesian Statistics: Databricks
    • Data Science Math Skills: Duke University
    • Improving your statistical inferences: Eindhoven University of Technology
    • Bayesian Statistics: Techniques and Models: University of California, Santa Cruz
    • Bayesian Statistics: Mixture Models: University of California, Santa Cruz
    • Bayesian Statistics: Time Series Analysis: University of California, Santa Cruz

    您可以在 Probability And Statistics 中学到的技能

    R 语言程序设计(中文版) (19)
    推断 (16)
    线性回归 (12)
    统计分析 (12)
    统计推断 (11)
    回归分析 (10)
    生物统计学 (9)
    贝叶斯定理 (7)
    逻辑回归 (7)
    概率分布 (7)
    贝叶斯统计 (6)
    医学统计 (6)

    关于 贝叶斯统计 的常见问题

    • Bayesian Statistics is an approach to statistics based on the work of the 18th century statistician and philosopher Thomas Bayes, and it is characterized by a rigorous mathematical attempt to quantify uncertainty. The likelihood of uncertain events is unknowable, by definition, but Bayes’s Theorem provides equations for the statistical inference of their probability based on prior information about an event - which can be updated based on the results of new data.

      While its origins lie hundreds of years in the past, Bayesian statistical approaches have become increasingly important in recent decades. The calculations at the heart of Bayesian statistics require intensive numerical integrations to solve, which were often infeasible before low-cost computing power became more widely accessible. But today, statisticians can evaluate integrals by running hundreds of thousands of simulation iterations with Markov chain Monte Carlo methods on an ordinary laptop computer.

      This new accessibility of computational power to quantify uncertainty has enabled Bayesian statistics to showcase its strength: making predictions. This capability is critical to many data science applications, and especially to the training of machine learning algorithms to create predictive analytics that assist with real-world decision-making problems. As with other areas of data science, statisticians often rely on R programming and Python programming skills to solve Bayesian equations.‎

    • Bayesian statistical approaches are essential to many data science and machine learning techniques, making an understanding of Bayes’ Theorem and related concepts essential to careers in these fields.

      If you wish to dive more deeply into the theoretical aspects of Bayesian statistics and the modeling of probability more generally, you can also pursue a career as a statistician. These experts may work in academia or the private sector, and usually have at least a master’s degree in mathematics or statistics. According to the Bureau of Labor Statistics, statisticians earn a median annual salary of $91,160.‎

    • Absolutely. Coursera gives you opportunities to learn about Bayesian statistics and related concepts in data science and machine learning through courses and Specializations from top-ranked schools like Duke University, the University of California, Santa Cruz, and the National Research University Higher School of Economics in Russia. You can also learn from industry leaders like Google Cloud, or through Coursera’s own exclusive Guided Projects, which let you build skills by completing step-by-step tutorials taught by expert instructors.

      Regardless of your needs, the combination of high-equality education, a flexible schedule, and low tuition costs leaves no uncertainty about the value of learning about Bayesian statistics on Coursera.‎

    • A background in statistics and certain areas of math, like algebra, can be extremely helpful when learning Bayesian statistics. This includes knowledge of and experience with statistical methods and statistical software. Any type of experience working with data, especially on a large scale, can also help. Classes, degrees, or work experience in biostatistics, psychometrics, analytics, quantitative psychology, banking, and public health can also be beneficial, especially if you plan to enter a career that centers around one of these topics or a related field. However, they aren't necessary for learning about Bayesian statistics in general.‎

    • People who aspire to work in roles that use Bayesian statistics should have analytical minds and a passion for using data to help other businesses and other people. You'll need good computer skills and a passion for statistics. You'll also need to be a good multitasker with excellent time management skills as well as someone who is highly organized. Good problem-solving skills are a must, as is flexibility. There are times when you may have total autonomy over your job and others when you're working with a team. That means you'll also need great interpersonal skills and the ability to communicate well, both verbally and in writing.‎

    • Anyone who works with data or seeks a career working with data may be interested in learning Bayesian statistics. Many companies that seek employees to work in fields involving statistics or big data prefer someone who understands and can implement the theories of Bayesian statistics to someone who can't. These companies typically offer competitive salaries and benefits and room for career advancement. Careers that may use Bayesian statistics also tend to have a good outlook for the future. Best of all, learning about this topic can open you up to jobs in numerous industries, ranging from banking and finance to health care and biostatistics.‎

    此常见问题解答内容仅供参考。建议学生多做研究,确保所追求的课程和其他证书符合他们的个人、专业和财务目标。
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