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4.6
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約21時間で修了

推奨:Four weeks of study, two-five hours/week depending on your familiarity with mathematical statistics....

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字幕:英語

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StatisticsBayesian StatisticsBayesian InferenceR Programming

100%オンライン

自分のスケジュールですぐに学習を始めてください。

柔軟性のある期限

スケジュールに従って期限をリセットします。

中級レベル

約21時間で修了

推奨:Four weeks of study, two-five hours/week depending on your familiarity with mathematical statistics....

英語

字幕:英語

シラバス - 本コースの学習内容

1
3時間で修了

Probability and Bayes' Theorem

In this module, we review the basics of probability and Bayes’ theorem. In Lesson 1, we introduce the different paradigms or definitions of probability and discuss why probability provides a coherent framework for dealing with uncertainty. In Lesson 2, we review the rules of conditional probability and introduce Bayes’ theorem. Lesson 3 reviews common probability distributions for discrete and continuous random variables....
8件のビデオ (合計38分), 4 readings, 5 quizzes
8件のビデオ
Lesson 1.1 Classical and frequentist probability6 分
Lesson 1.2 Bayesian probability and coherence3 分
Lesson 2.1 Conditional probability4 分
Lesson 2.2 Bayes' theorem6 分
Lesson 3.1 Bernoulli and binomial distributions5 分
Lesson 3.2 Uniform distribution5 分
Lesson 3.3 Exponential and normal distributions2 分
4件の学習用教材
Module 1 objectives, assignments, and supplementary materials3 分
Background for Lesson 110 分
Supplementary material for Lesson 23 分
Supplementary material for Lesson 320 分
5の練習問題
Lesson 116 分
Lesson 212 分
Lesson 3.120 分
Lesson 3.2-3.310 分
Module 1 Honors15 分
2
3時間で修了

Statistical Inference

This module introduces concepts of statistical inference from both frequentist and Bayesian perspectives. Lesson 4 takes the frequentist view, demonstrating maximum likelihood estimation and confidence intervals for binomial data. Lesson 5 introduces the fundamentals of Bayesian inference. Beginning with a binomial likelihood and prior probabilities for simple hypotheses, you will learn how to use Bayes’ theorem to update the prior with data to obtain posterior probabilities. This framework is extended with the continuous version of Bayes theorem to estimate continuous model parameters, and calculate posterior probabilities and credible intervals....
11件のビデオ (合計59分), 5 readings, 4 quizzes
11件のビデオ
Lesson 4.2 Likelihood function and maximum likelihood7 分
Lesson 4.3 Computing the MLE3 分
Lesson 4.4 Computing the MLE: examples4 分
Introduction to R6 分
Plotting the likelihood in R4 分
Plotting the likelihood in Excel4 分
Lesson 5.1 Inference example: frequentist4 分
Lesson 5.2 Inference example: Bayesian6 分
Lesson 5.3 Continuous version of Bayes' theorem4 分
Lesson 5.4 Posterior intervals7 分
5件の学習用教材
Module 2 objectives, assignments, and supplementary materials3 分
Background for Lesson 410 分
Supplementary material for Lesson 45 分
Background for Lesson 510 分
Supplementary material for Lesson 510 分
4の練習問題
Lesson 48 分
Lesson 5.1-5.218 分
Lesson 5.3-5.416 分
Module 2 Honors6 分
3
2時間で修了

Priors and Models for Discrete Data

In this module, you will learn methods for selecting prior distributions and building models for discrete data. Lesson 6 introduces prior selection and predictive distributions as a means of evaluating priors. Lesson 7 demonstrates Bayesian analysis of Bernoulli data and introduces the computationally convenient concept of conjugate priors. Lesson 8 builds a conjugate model for Poisson data and discusses strategies for selection of prior hyperparameters....
9件のビデオ (合計66分), 2 readings, 4 quizzes
9件のビデオ
Lesson 6.2 Prior predictive: binomial example5 分
Lesson 6.3 Posterior predictive distribution4 分
Lesson 7.1 Bernoulli/binomial likelihood with uniform prior3 分
Lesson 7.2 Conjugate priors4 分
Lesson 7.3 Posterior mean and effective sample size7 分
Data analysis example in R12 分
Data analysis example in Excel16 分
Lesson 8.1 Poisson data8 分
2件の学習用教材
Module 3 objectives, assignments, and supplementary materials3 分
R and Excel code from example analysis10 分
4の練習問題
Lesson 612 分
Lesson 715 分
Lesson 815 分
Module 3 Honors8 分
4
3時間で修了

Models for Continuous Data

This module covers conjugate and objective Bayesian analysis for continuous data. Lesson 9 presents the conjugate model for exponentially distributed data. Lesson 10 discusses models for normally distributed data, which play a central role in statistics. In Lesson 11, we return to prior selection and discuss ‘objective’ or ‘non-informative’ priors. Lesson 12 presents Bayesian linear regression with non-informative priors, which yield results comparable to those of classical regression. ...
9件のビデオ (合計69分), 5 readings, 5 quizzes
9件のビデオ
Lesson 10.1 Normal likelihood with variance known3 分
Lesson 10.2 Normal likelihood with variance unknown3 分
Lesson 11.1 Non-informative priors8 分
Lesson 11.2 Jeffreys prior3 分
Linear regression in R17 分
Linear regression in Excel (Analysis ToolPak)13 分
Linear regression in Excel (StatPlus by AnalystSoft)14 分
Conclusion1 分
5件の学習用教材
Module 4 objectives, assignments, and supplementary materials3 分
Supplementary material for Lesson 1010 分
Supplementary material for Lesson 115 分
Background for Lesson 1210 分
R and Excel code for regression5 分
5の練習問題
Lesson 912 分
Lesson 1020 分
Lesson 1110 分
Regression15 分
Module 4 Honors6 分
4.6
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人気のレビュー

by GSSep 1st 2017

Good intro to Bayesian Statistics. Covers the basic concepts. Workload is reasonable and quizzes/exercises are helpful. Could include more exercises and additional backgroung/future reading materials.

by JHJun 27th 2018

Great course. The content moves at a nice pace and the videos are really good to follow. The Quizzes are also set at a good level. You can't pass this course unless you have understood the material.

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Herbert Lee

Professor
Applied Mathematics and Statistics

カリフォルニア大学サンタクルーズ校(University of California, Santa Cruz)について

UC Santa Cruz is an outstanding public research university with a deep commitment to undergraduate education. It’s a place that connects people and programs in unexpected ways while providing unparalleled opportunities for students to learn through hands-on experience....

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  • You should have exposure to the concepts from a basic statistics class (for example, probability, the Central Limit Theorem, confidence intervals, linear regression) and calculus (integration and differentiation), but it is not expected that you remember how to do all of these items. The course will provide some overview of the statistical concepts, which should be enough to remind you of the necessary details if you've at least seen the concepts previously. On the calculus side, the lectures will include some use of calculus, so it is important that you understand the concept of an integral as finding the area under a curve, or differentiating to find a maximum, but you will not be required to do any integration or differentiation yourself.

  • Data analysis is done using computer software. This course provides the option of Excel or R. Equivalent content is provided for both options. A very brief introduction to R is provided for people who have never used it before, but this is not meant to be a course on R. Learners using Excel are expected to already have basic familiarity of Excel.

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