Topics in Model Performance-Metrics to assess model performance - I
Welcome to Course 2!
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What can you expect from this course/specialization?
Introduction
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Underfitting and Overfitting
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Explained Variance
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Cross Validation
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Information Criteria
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Log-likelihood and Deviance
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Posterior Predictive Distribution
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Likelihood and its use in Parameter Estimation and Model Comparison
Topics in Model Performance-Metrics to assess model performance - II
AIC, BIC, DIC and WAIC
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A qualitative discussion of the various metrics
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Entropy
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KL Divergence
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Model Averaging
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Understanding predictive information criteria for Bayesian models
Information Theory and Statistics
Model Stacking
The Metropolis Algorithms for MCMC-The Foundations of Bayesian Inference
Introduction
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Markov Chains
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Why does Markov Chain Monte Carlo work?
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Markov Chains
The Metropolis Algorithms for MCMC-The Metropolis Algorithm
The Metropolis algorithm for sampling
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The Metropolis algorithm in detail
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Building the inferred distribution
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Implementing the Metropolis algorithm in Python
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The Metropolis Algorithms for MCMC-The Metropolis-Hastings Algorithm
The Metropolis-Hastings algorithm
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Gibbs Sampling and Hamiltonian Monte Carlo Algorithms-Gibbs Sampling
Introduction to Gibbs sampling
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Overview of the Gibbs Sampling algorithm
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The Gibbs sampling algorithm in detail
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Gibbs Sampling and Hamiltonian Monte Carlo Algorithms-Hamiltonian Monte Carlo
Introduction to Hamiltonian Monte Carlo
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The Hamiltonian Monte Carlo algorithm in detail
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Hamiltonian Monte Carlo
HMC on Stan
Gibbs Sampling and Hamiltonian Monte Carlo Algorithms-Characteristics of MCMC
Properties of MCMC - I
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Properties of MCMC - II
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