Introduction to Bayesian Analysis Course with Python 2021

[LeeAndro]

MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 88 lectures (12h 54m) | Size: 4.67 GB

Learn the concepts and practical side of using the Bayesian approach to estimate likely event outcomes.

PyMC3.

posterior

ROPE

Loss functions

Gaussian

Gaussian inferences

Student's t-distribution

Groups comparison

Hierarchical models

Shrinkage

Linear models and high autocorrelation

Pearson correlation coefficient

Pearson coefficient from a multivariate Gaussian

Robust linear regression

Hierarchical linear regression

Correlation, causation, and the messiness of life

Polynomial regression

Confounding variables and redundant variables

Masking effect variables

Variable variance

Adding interactions

Logistic regression

Multiple logistic regression

Dealing with correlated variables

Dealing with unbalanced classes

Softmax regression

Discriminative and generative models

the zero-inflated Poisson model

Posterior predictive checks

Occam's razor - simplicity and accuracy

Model averaging

Bayes factors

Non-identifiability of mixture models

How to choose K values

Python knowledge is required

This course is a comprehensive guide to Bayesian Statistics. It includes video explanations along with real life illustrations, examples, numerical problems, and take away notes. The course covers the basic theory behind probabilistic and Bayesian modelling, and their applications to common problems in data science, business, and applied sciences.

The course is divided into the following sections:

Section 2 and 3: These two sections cover the concepts that are crucial to understand the basics of Bayesian Statistics-

Introduction to Bayesian Probability

Introduction to PyMC3 pr

Summarizing the posterior.

Introduction to ROPE.

introduction to Gaussian.

Student's t-distribution.

Hierarchical models Introduction.

Linear models and high autocorrelation.

Introduction to Pearson coefficient from a multivariate Gaussian.

Robust linear regression.

Hierarchical linear regression.

Correlation, causation, and the messiness of life.

Polynomial regression.

Introduction to Confounding variables and redundant variables.

Masking effect variables.

Adding interactions.

Variable variance.

Section 4: This section covers Linear model generalization:

Introduction to Generalizing linear models.

Introduction to Logistic regression.

Applying the logistic regression to The Iris dataset.

Multiple logistic regression.

Interpreting the coefficients of a logistic regression.

Dealing with correlated variables.

Dealing with unbalanced classes.

Introduction to Softmax regression.

Introduction to Discriminative and generative models.

Introduction to Poisson regression.

Introduction to The zero-inflated Poisson model.

Section 5: This section covers Model Comparison:

Posterior predictive checks Implementation.

Occam's razor - simplicity and accuracy.

Model comparison with PyMC3.

Introduction to Bayes factors.

Bayes factors Implementation.

Common problems when computing Bayes factors and solutions.

Regularizing priors.

Section 6: This section covers Mixture Models

Introduction to Finite mixture models and its implementation.

How to choose K values.

Comparing models.

Mixture models and clustering.

Introduction to Continuous mixtures

At the end of the course, you will have a complete understanding of Bayesian concepts from scratch. You will know how to effectively use Bayesian approach and think probabilistically. Enrolling in this course will make it easier for you to score well in your exams or apply Bayesian approach elsewhere.

Complete this course, master the principles, and join the queue of top Statistics students all around the world.

The course is ideal for anyone interested in learning both the conceptual and practical side of using Bayes' Rule to model likely event outcomes.

The course is best suited for both students and professionals who currently make use of quantitative or probabilistic modelling.

Students currently pursuing Statistics and Probability.

Anyone who wants to build a strong fundamental of Bayesian Statistics.

Anyone who wants to apply Bayesian Statistics to other fields like ML, Artificial Intelligence, Business, Applied Sciences, Psychology. etc.

Students of Machine Learning and Data Science.

Data Scientists curious about Bayesian Statistics.



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