Este curso forma parte de Programa especializado: Estadística bayesiana

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estrellas
3,052 calificaciones

91 %

Herbert Lee

138.859 ya inscrito

Ofrecido Por

Programa especializado: Estadística bayesianaUniversidad de California en Santa Cruz

Acerca de este Curso

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This course introduces the Bayesian approach to statistics, starting with the concept of probability and moving to the analysis of data. We will learn about the philosophy of the Bayesian approach as well as how to implement it for common types of data. We will compare the Bayesian approach to the more commonly-taught Frequentist approach, and see some of the benefits of the Bayesian approach. In particular, the Bayesian approach allows for better accounting of uncertainty, results that have more intuitive and interpretable meaning, and more explicit statements of assumptions. This course combines lecture videos, computer demonstrations, readings, exercises, and discussion boards to create an active learning experience. For computing, you have the choice of using Microsoft Excel or the open-source, freely available statistical package R, with equivalent content for both options. The lectures provide some of the basic mathematical development as well as explanations of philosophy and interpretation. Completion of this course will give you an understanding of the concepts of the Bayesian approach, understanding the key differences between Bayesian and Frequentist approaches, and the ability to do basic data analyses.

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Curso 1 de 5 en

Programa especializado: Estadística bayesiana

Nivel intermedio

Aprox. 11 horas para completar

Inglés (English)

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Habilidades que obtendrás

Statistics
Bayesian Statistics
Bayesian Inference
R Programming

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Certificado para compartir

Obtén un certificado al finalizar

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Curso 1 de 5 en

Programa especializado: Estadística bayesiana

Nivel intermedio

Aprox. 11 horas para completar

Inglés (English)

¿Podría tu empresa beneficiarse de la capacitación de los empleados en las habilidades más demandadas?

Prueba Coursera para negocios

Instructor

Calificación del instructor

4,58/5 (461 calificaciones)

Herbert Lee

Ofrecido por

Universidad de California en Santa Cruz

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Programa - Qué aprenderás en este curso

Calificación del contenido91%(10,863 calificaciones)

Semana1

Semana 1

3 horas para completar

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.

3 horas para completar

8 videos (Total 38 minutos), 4 lecturas, 5 cuestionarios

8 videos

Course introduction4mLesson 1.1 Classical and frequentist probability6mLesson 1.2 Bayesian probability and coherence3mLesson 2.1 Conditional probability4mLesson 2.2 Bayes' theorem6mLesson 3.1 Bernoulli and binomial distributions5mLesson 3.2 Uniform distribution5mLesson 3.3 Exponential and normal distributions2m

4 lecturas

Module 1 objectives, assignments, and supplementary materials3mBackground for Lesson 110mSupplementary material for Lesson 23mSupplementary material for Lesson 320m

5 ejercicios de práctica

Lesson 1: Demonstrate your knowledge30mLesson 2: Demonstrate your knowledge12mLesson 3.1: Demonstrate your knowledge30mLesson 3.2-3.3: Demonstrate your knowledge10mModule 1 Honors15m

Semana2

Semana 2

3 horas para completar

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.

3 horas para completar

11 videos (Total 59 minutos), 5 lecturas, 4 cuestionarios

11 videos

Lesson 4.1 Confidence intervals4mLesson 4.2 Likelihood function and maximum likelihood7mLesson 4.3 Computing the MLE3mLesson 4.4 Computing the MLE: examples4mIntroduction to R6mPlotting the likelihood in R4mPlotting the likelihood in Excel4mLesson 5.1 Inference example: frequentist4mLesson 5.2 Inference example: Bayesian6mLesson 5.3 Continuous version of Bayes' theorem4mLesson 5.4 Posterior intervals7m

5 lecturas

Module 2 objectives, assignments, and supplementary materials3mBackground for Lesson 410mSupplementary material for Lesson 45mBackground for Lesson 510mSupplementary material for Lesson 510m

4 ejercicios de práctica

Lesson 4: Demonstrate your knowledge8mLesson 5.1-5.2: Demonstrate your knowledge30mLesson 5.3-5.4: Demonstrate your knowledge30mModule 2 Honors6m

Semana3

Semana 3

3 horas para completar

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.

3 horas para completar

9 videos (Total 66 minutos), 2 lecturas, 4 cuestionarios

9 videos

Lesson 6.1 Priors and prior predictive distributions3mLesson 6.2 Prior predictive: binomial example5mLesson 6.3 Posterior predictive distribution4mLesson 7.1 Bernoulli/binomial likelihood with uniform prior3mLesson 7.2 Conjugate priors4mLesson 7.3 Posterior mean and effective sample size7mData analysis example in R12mData analysis example in Excel16mLesson 8.1 Poisson data8m

2 lecturas

Module 3 objectives, assignments, and supplementary materials3mR and Excel code from example analysis10m

4 ejercicios de práctica

Lesson 6: Demonstrate your knowledge30mLesson 7: Demonstrate your knowledge15mLesson 8: Demonstrate your knowledge15mModule 3 Honors8m

Semana4

Semana 4

3 horas para completar

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.

3 horas para completar

9 videos (Total 69 minutos), 5 lecturas, 5 cuestionarios

9 videos

Lesson 9.1 Exponential data4mLesson 10.1 Normal likelihood with variance known3mLesson 10.2 Normal likelihood with variance unknown3mLesson 11.1 Non-informative priors8mLesson 11.2 Jeffreys prior3mLinear regression in R17mLinear regression in Excel (Analysis ToolPak)13mLinear regression in Excel (StatPlus by AnalystSoft)14mConclusion1m

5 lecturas

Module 4 objectives, assignments, and supplementary materials3mSupplementary material for Lesson 1010mSupplementary material for Lesson 115mBackground for Lesson 1210mR and Excel code for regression5m

5 ejercicios de práctica

Lesson 9: Demonstrate your knowledge12mLesson 10: Demonstrate your knowledge20mLesson 11: Demonstrate your knowledge10mRegression: Demonstrate your knowledge15mModule 4 Honors6m

Reseñas

4.6

800 reseña

5 stars
66,98 %
4 stars
25,45 %
3 stars
5,26 %
2 stars
1,34 %
1 star
0,94 %

Principales reseñas sobre BAYESIAN STATISTICS: FROM CONCEPT TO DATA ANALYSIS

por YH21 de dic. de 2018

Very concise and helpful for an intro to Bayesian statistics. Good level of difficulty to encourage learning. This well prepares further study of more advanced topics such as MCMC and more.

por BU24 de may. de 2020

This was an invaluable learning experience. I was delighted to go through this learning.

I strongly suggest this course to anyone who wants to learn or refresh their statistics knowledge.

por MD18 de feb. de 2020

the notes for the lectures are missing.

In my opinion the notes, which includes the video materials could be very useful.

the course was good. I learnt some new concepts in bayesian thinking.

por SP21 de nov. de 2020

Very insightful. I'd recommend this course to anyone who wants to learn how to adjust a model after observing data. Test questions were quite practical modeling real world scenarios.

Ver todas las reseñas

Acerca de Programa especializado: Estadística bayesiana

This Specialization is intended for all learners seeking to develop proficiency in statistics, Bayesian statistics, Bayesian inference, R programming, and much more. Through four complete courses (From Concept to Data Analysis; Techniques and Models; Mixture Models; Time Series Analysis) and a culminating project, you will cover Bayesian methods — such as conjugate models, MCMC, mixture models, and dynamic linear modeling — which will provide you with the skills necessary to perform analysis, engage in forecasting, and create statistical models using real-world data.

Preguntas Frecuentes

¿Cuándo podré acceder a las lecciones y tareas?
El acceso a las clases y las asignaciones depende del tipo de inscripción que tengas. Si tomas un curso en modo de oyente, verás la mayoría de los materiales del curso en forma gratuita. Para acceder a asignaciones calificadas y obtener un certificado, deberás comprar la experiencia de Certificado, ya sea durante o después de participar como oyente. Si no ves la opción de oyente:
es posible que el curso no ofrezca la opción de participar como oyente. En cambio, puedes intentar con una Prueba gratis o postularte para recibir ayuda económica.
Es posible que el curso ofrezca la opción 'Curso completo, sin certificado'. Esta opción te permite ver todos los materiales del curso, enviar las evaluaciones requeridas y obtener una calificación final. También significa que no podrás comprar una experiencia de Certificado.
¿Qué recibiré si me suscribo a este Programa especializado?
Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.
¿Hay ayuda económica disponible?
Sí. En ciertos programas de aprendizaje, puedes postularte para recibir ayuda económica o una beca en caso de no poder costear los gastos de la tarifa de inscripción. Si hay ayuda económica o becas disponibles para tu selección de programa de aprendizaje, verás un enlace para postularte en la página de descripción.
What are the pre-requisites for this course?
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.
What computing resources are expected for this course?
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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Bayesian Statistics: From Concept to Data Analysis

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Habilidades que obtendrás