Terms Index

HMC

• The Idea of Hamiltonian Monte Carlo [LINK]

MCMC

• Formal Tests for Convergence [LINK]
• The Idea of Hamiltonian Monte Carlo [LINK]

advi

• ADVI [LINK]
• Gaussian Mixture Model with ADVI [LINK]

aic

• Model Comparison continued [LINK]
• Model Comparison [LINK]
• Understanding AIC [LINK]

autocorrelation

• Convergence and Coverage [LINK]
• Intro to Gibbs Sampling [LINK]
• A gibbs sampler with lots of autocorrelation [LINK]

backpropagation

• Maximum Likelihood Estimation [LINK]

bayes risk

• Classification Risk [LINK]
• Utility or Risk - Back to optimization [LINK]

bayes theorem

• Probability [LINK]

bayesian

• Bayesian Statistics [LINK]
• Sampling with $\sigma$ [LINK]
• Bayesian Regression [LINK]
• Formal Tests for Convergence [LINK]
• The beta-binomial model of globe-tossing [LINK]
• Hierarchical Models [LINK]
• Identifiability [LINK]
• Levels of Bayesian Analysis [LINK]
• The normal model [LINK]
• The normal model with pymc [LINK]
• Priors [LINK]
• From the normal model to regression [LINK]
• Regression with custom priors [LINK]
• Sufficient Statistics and Exchangeability [LINK]
• Imputation and Convergence [LINK]
• Unidentifiability, the ridge, and the lasso [LINK]

bayesian p-values

• Model checking [LINK]

bayesian updating

• Bayesian Regression [LINK]
• The beta-binomial model of globe-tossing [LINK]

bayesian workflow

• Bayesian Workflow in the 0-inflated model [LINK]

bernoulli distribution

• Distributions example - elections [LINK]
• Distributions [LINK]

beta

• Gibbs with conditional a conjugate [LINK]
• The beta-binomial model of globe-tossing [LINK]
• Hierarchical Models [LINK]

beta-binomial

• Gibbs with conditional a conjugate [LINK]
• The beta-binomial model of globe-tossing [LINK]
• Hierarchical Models [LINK]

bias

• Learning a model [LINK]
• Noisy Learning [LINK]

bias-variance tradeoff

• Noisy Learning [LINK]

binomial

• Entropy [LINK]
• Distributions example - elections [LINK]
• Gibbs with conditional a conjugate [LINK]
• The beta-binomial model of globe-tossing [LINK]
• Hierarchical Models [LINK]

binomial regression

• Lab 7 - Bayesian inference with PyMC3. [LINK]

bioassay

• Lab 7 - Bayesian inference with PyMC3. [LINK]

boltzmann distribution

• From Annealing To Metropolis [LINK]
• Simulated Annealing [LINK]

bootstrap

• Frequentist Stats [LINK]

box loop

• Box's Loop [LINK]

box-muller

• The Inverse Transform [LINK]

calculus

• Calculus for optimization [LINK]

canonical distribution

• Exploring Hamiltonian Monte Carlo [LINK]
• L, epsilon, and other tweaking [LINK]

cavi

• Variational Inference [LINK]

cdf

• Distributions [LINK]
• The Inverse Transform [LINK]

centering

• Poisson Regression - tools on islands part 2 [LINK]

central limit theorem

• Sampling and the Central Limit Theorem [LINK]
• Distributions example - elections [LINK]
• Monte Carlo Integrals [LINK]

classical mechanics

• Exploring Hamiltonian Monte Carlo [LINK]
• L, epsilon, and other tweaking [LINK]

classification

• Classification Risk [LINK]

combinatoric optimization

• Simulated Annealing [LINK]

complexity parameter

• Regularization [LINK]
• Learning bounds and the Test set [LINK]
• Why do we need validation? [LINK]

conditional

• The idea behind the GP [LINK]
• Gaussian Processes and 'Non-parametric' Bayes [LINK]
• Distributions [LINK]
• Inference for GPs [LINK]
• Probability [LINK]

conjugate prior

• Bayesian Regression [LINK]
• Gibbs with conditional a conjugate [LINK]
• The beta-binomial model of globe-tossing [LINK]
• The normal model [LINK]
• The normal model with pymc [LINK]
• Priors [LINK]
• From the normal model to regression [LINK]
• Sufficient Statistics and Exchangeability [LINK]

convex

• Convexity and Jensen's Inequality [LINK]

correlation

• The idea behind the GP [LINK]
• Gaussian Processes and 'Non-parametric' Bayes [LINK]
• Correlations [LINK]
• Inference for GPs [LINK]
• Geographic Correlation and Oceanic Tools [LINK]

counterfactual plot

• Poisson Regression - tools on islands part 2 [LINK]

covariance

• The idea behind the GP [LINK]
• Gaussian Processes and 'Non-parametric' Bayes [LINK]
• Correlations [LINK]
• Inference for GPs [LINK]
• Geographic Correlation and Oceanic Tools [LINK]

cross-entropy

• Divergence and Deviance [LINK]

cross-validation

• Model Comparison continued [LINK]
• Model Comparison [LINK]
• Regularization [LINK]
• Why do we need validation? [LINK]

curse of dimensionality

• From Annealing To Metropolis [LINK]

data augmentation

• Data Augmentation [LINK]
• Exploring Hamiltonian Monte Carlo [LINK]
• L, epsilon, and other tweaking [LINK]
• Slice Sampling [LINK]

decision risk

• Classification Risk [LINK]
• Utility or Risk - Back to optimization [LINK]

decision theory

• Classification Risk [LINK]
• The beta-binomial model of globe-tossing [LINK]
• Utility or Risk - Back to optimization [LINK]

detailed balance

• Exploring Hamiltonian Monte Carlo [LINK]
• L, epsilon, and other tweaking [LINK]
• Markov Chains and MCMC [LINK]
• From Annealing To Metropolis [LINK]
• Metropolis-Hastings [LINK]

deterministic error

• Learning a model [LINK]

deviance

• Divergence and Deviance [LINK]
• Model Comparison continued [LINK]
• Model Comparison [LINK]
• Understanding AIC [LINK]

dic

• Model Comparison continued [LINK]
• Model Comparison [LINK]

discrepancy

• Model checking [LINK]

discrete sampling

• Sampling from a discrete distribution [LINK]
• Metropolis-Hastings [LINK]

discriminative models

• Machine learning- ERM and Bayesian [LINK]

distributions

• Expectations and the Law of Large Numbers [LINK]

divergences

• Gelman Schools and Hierarchical Pathology [LINK]
• Gelman Schools Theory for Topics about Restaurants [LINK]
• L, epsilon, and other tweaking [LINK]

effective sample size

• A gibbs sampler with lots of autocorrelation [LINK]

elbo

• The EM algorithm [LINK]
• Variational Inference [LINK]
• ADVI [LINK]
• Gaussian Mixture Model with ADVI [LINK]

elections

• Distributions example - elections [LINK]

empirical bayes

• Inference for GPs [LINK]
• Hierarchical Models [LINK]
• Model Comparison continued [LINK]
• Model Comparison [LINK]

empirical distribution

• Distributions example - elections [LINK]

empirical risk minimization

• Machine learning- ERM and Bayesian [LINK]
• Learning a model [LINK]
• Noisy Learning [LINK]
• Learning bounds and the Test set [LINK]
• Why do we need validation? [LINK]

energy

• Exploring Hamiltonian Monte Carlo [LINK]
• The Idea of Hamiltonian Monte Carlo [LINK]
• L, epsilon, and other tweaking [LINK]
• Simulated Annealing [LINK]

entropy

• Divergence and Deviance [LINK]
• Entropy [LINK]

equilibrium

• From Annealing To Metropolis [LINK]

ergodicity

• Markov Chains and MCMC [LINK]

estimation risk

• Utility or Risk - Back to optimization [LINK]

exchangeability

• Hierarchical Models [LINK]
• Sufficient Statistics and Exchangeability [LINK]

expectations

• Expectations and the Law of Large Numbers [LINK]
• Basic Monte Carlo [LINK]
• Importance Sampling [LINK]

exponential distribution

• Maximum Likelihood Estimation [LINK]

exponential family

• Sufficient Statistics and Exchangeability [LINK]

formal tests

• Formal Tests for Convergence [LINK]
• Imputation and Convergence [LINK]

frequentist statistics

• Frequentist Stats [LINK]

full-data likelihood

• The EM algorithm [LINK]

gamma

• Sufficient Statistics and Exchangeability [LINK]

gaussian mixture model

• Variational Inference [LINK]
• Gaussian Mixture Model with ADVI [LINK]
• Marginalizing over Discretes [LINK]
• Types of learning and MCMC [LINK]
• Mixtures and MCMC [LINK]
• Mixture Models, and types of learning [LINK]

gaussian process

• The idea behind the GP [LINK]
• Gaussian Processes and 'Non-parametric' Bayes [LINK]
• Inference for GPs [LINK]
• Geographic Correlation and Oceanic Tools [LINK]

gelman-rubin

• Lab 7 - Bayesian inference with PyMC3. [LINK]

generative model

• Machine learning- ERM and Bayesian [LINK]
• Mixture Models, and types of learning [LINK]

gewecke

• Lab 7 - Bayesian inference with PyMC3. [LINK]

gibbs sampler

• Data Augmentation [LINK]
• Gibbs with conditional a conjugate [LINK]
• Gibbs from Metropolis-Hastings [LINK]
• Intro to Gibbs Sampling [LINK]
• A gibbs sampler with lots of autocorrelation [LINK]

glm

• Poisson Regression - tools on islands part 2 [LINK]
• Generalized Linear Models [LINK]
• Poisson and 0-inflated [LINK]
• Bayesian Workflow in the 0-inflated model [LINK]
• Prosocial Chimps [LINK]

global minimum

• Simulated Annealing [LINK]

gradient descent

• Gradient Descent and SGD [LINK]
• Lab 3 - Pytorch [LINK]
• Mathlab (TM) [LINK]
• Calculus for optimization [LINK]

hamiltonian monte carlo

• Exploring Hamiltonian Monte Carlo [LINK]
• L, epsilon, and other tweaking [LINK]

hierarchical

• Poisson Regression - tools on islands part 2 [LINK]
• Lab 7 - Bayesian inference with PyMC3. [LINK]
• Gelman Schools and Hierarchical Pathology [LINK]
• Gelman Schools Theory for Topics about Restaurants [LINK]
• Hierarchical Models [LINK]
• L, epsilon, and other tweaking [LINK]

hierarchical normal-normal model

• Gelman Schools and Hierarchical Pathology [LINK]
• Gelman Schools Theory for Topics about Restaurants [LINK]

hoeffding's inequality

• Learning bounds and the Test set [LINK]
• Why do we need validation? [LINK]

hypothesis space

• Learning a model [LINK]
• Noisy Learning [LINK]
• Learning bounds and the Test set [LINK]
• Why do we need validation? [LINK]

identifiability

• Identifiability [LINK]
• Types of learning and MCMC [LINK]
• Mixtures and MCMC [LINK]
• Unidentifiability, the ridge, and the lasso [LINK]

importance sampling

• Importance Sampling [LINK]

imputation

• Imputation and Convergence [LINK]

in-sample

• Model Comparison continued [LINK]
• Model Comparison [LINK]

independence

• Probability [LINK]

inference

• Maximum Likelihood Estimation [LINK]
• Maximum Likelihood Estimation [LINK]
• Inference for GPs [LINK]

infinite-basis

• Gaussian Processes and 'Non-parametric' Bayes [LINK]

integration

• Basic Monte Carlo [LINK]
• Importance Sampling [LINK]
• Mathlab (TM) [LINK]
• Monte Carlo Integrals [LINK]

interaction-term

• Poisson Regression - tools on islands part 2 [LINK]

inverse transform

• The Inverse Transform [LINK]

irreducible

• Markov Chains and MCMC [LINK]
• From Annealing To Metropolis [LINK]

jeffreys prior

• Priors [LINK]
• Regression with custom priors [LINK]

jensen's inequality

• Divergence and Deviance [LINK]
• Convexity and Jensen's Inequality [LINK]

kernel

• The idea behind the GP [LINK]
• Gaussian Processes and 'Non-parametric' Bayes [LINK]
• Inference for GPs [LINK]

kernel trick

• Gaussian Processes and 'Non-parametric' Bayes [LINK]

kernelized regression

• Gaussian Processes and 'Non-parametric' Bayes [LINK]

kidney cancer

• Hierarchical Models [LINK]

kl-divergence

• Divergence and Deviance [LINK]
• Variational Inference [LINK]
• ADVI [LINK]
• Model Comparison continued [LINK]
• Model Comparison [LINK]

label-switching

• Types of learning and MCMC [LINK]
• Mixtures and MCMC [LINK]

lasso

• Regularization [LINK]
• Unidentifiability, the ridge, and the lasso [LINK]

latent variables

• The EM algorithm [LINK]
• Variational Inference [LINK]
• Mixture Models, and types of learning [LINK]

law of large numbers

• Expectations and the Law of Large Numbers [LINK]
• Basic Monte Carlo [LINK]
• Monte Carlo Integrals [LINK]
• Learning a model [LINK]

leapfrog

• Exploring Hamiltonian Monte Carlo [LINK]
• L, epsilon, and other tweaking [LINK]

likelihood

• Divergence and Deviance [LINK]

likelihood-ratio

• Divergence and Deviance [LINK]

linear regression

• Maximum Likelihood Estimation [LINK]
• Gradient Descent and SGD [LINK]
• Regularization [LINK]

link-function

• Generalized Linear Models [LINK]
• Poisson and 0-inflated [LINK]
• Bayesian Workflow in the 0-inflated model [LINK]

lkj prior

• Correlations [LINK]

log-likelihood

• Divergence and Deviance [LINK]
• The EM algorithm [LINK]

log-sum-exp trick

• Marginalizing over Discretes [LINK]

logistic regression

• Maximum Likelihood Estimation [LINK]
• Maximum Likelihood Estimation [LINK]
• Lab 3 - Pytorch [LINK]

loocv

• Model Comparison continued [LINK]
• Model Comparison [LINK]

loss function

• The beta-binomial model of globe-tossing [LINK]

lotus

• Expectations and the Law of Large Numbers [LINK]
• Basic Monte Carlo [LINK]
• Monte Carlo Integrals [LINK]

machine learning

• Machine learning- ERM and Bayesian [LINK]

map

• Bayesian Statistics [LINK]
• Sampling with $\sigma$ [LINK]
• Unidentifiability, the ridge, and the lasso [LINK]

marginal

• The idea behind the GP [LINK]
• Gaussian Processes and 'Non-parametric' Bayes [LINK]
• Distributions [LINK]
• Gaussian Mixture Model with ADVI [LINK]
• Inference for GPs [LINK]
• Probability [LINK]

marginal energy distribution

• Exploring Hamiltonian Monte Carlo [LINK]
• L, epsilon, and other tweaking [LINK]

marginalizing over discretes

• Gaussian Mixture Model with ADVI [LINK]
• Marginalizing over Discretes [LINK]

markov chain

• Markov Chains and MCMC [LINK]
• Simulated Annealing [LINK]

maxent

• Entropy [LINK]

maximum likelihood

• The EM algorithm [LINK]
• Maximum Likelihood Estimation [LINK]
• Maximum Likelihood Estimation [LINK]
• Frequentist Stats [LINK]

mcmc

• Wrongly combining rejection with sampling [LINK]
• Bayesian Statistics [LINK]
• Sampling with $\sigma$ [LINK]
• Convergence and Coverage [LINK]
• Data Augmentation [LINK]
• Sampling from a discrete distribution [LINK]
• Gibbs with conditional a conjugate [LINK]
• Gibbs from Metropolis-Hastings [LINK]
• Identifiability [LINK]
• Intro to Gibbs Sampling [LINK]
• Simulated Annealing [LINK]
• Markov Chains and MCMC [LINK]
• Metropolis-Hastings [LINK]
• Priors [LINK]
• Regression with custom priors [LINK]
• Slice Sampling [LINK]
• Imputation and Convergence [LINK]
• A gibbs sampler with lots of autocorrelation [LINK]
• Unidentifiability, the ridge, and the lasso [LINK]

mcmc engineering

• Formal Tests for Convergence [LINK]
• The normal model with pymc [LINK]
• From the normal model to regression [LINK]
• Imputation and Convergence [LINK]

mean-field approximation

• Variational Inference [LINK]
• ADVI [LINK]

mercer's theorem

• Gaussian Processes and 'Non-parametric' Bayes [LINK]

metropolis

• Wrongly combining rejection with sampling [LINK]
• Convergence and Coverage [LINK]
• Sampling from a discrete distribution [LINK]
• Gibbs from Metropolis-Hastings [LINK]
• Markov Chains and MCMC [LINK]
• From Annealing To Metropolis [LINK]
• Metropolis-Hastings [LINK]
• Priors [LINK]

metropolis-hastings

• Wrongly combining rejection with sampling [LINK]
• Convergence and Coverage [LINK]
• Gibbs from Metropolis-Hastings [LINK]
• Simulated Annealing [LINK]
• Markov Chains and MCMC [LINK]
• Metropolis-Hastings [LINK]

microcanonical distribution

• Exploring Hamiltonian Monte Carlo [LINK]
• L, epsilon, and other tweaking [LINK]

minibatch sgd

• ADVI [LINK]
• Gradient Descent and SGD [LINK]
• Lab 3 - Pytorch [LINK]

mixture model

• The EM algorithm [LINK]
• Variational Inference [LINK]
• Gaussian Mixture Model with ADVI [LINK]
• Marginalizing over Discretes [LINK]
• Types of learning and MCMC [LINK]
• Mixtures and MCMC [LINK]
• Poisson and 0-inflated [LINK]
• Bayesian Workflow in the 0-inflated model [LINK]
• Mixture Models, and types of learning [LINK]

mlp

• MLP as universal approximator [LINK]

model averaging

• Model Comparison continued [LINK]
• Model Comparison [LINK]
• Prosocial Chimps [LINK]
• Utility or Risk - Back to optimization [LINK]

model checking

• Model checking [LINK]

model comparison

• Prosocial Chimps [LINK]

model-comparison

• Poisson Regression - tools on islands part 2 [LINK]
• Model Comparison continued [LINK]
• Model Comparison [LINK]

monte-carlo

• Basic Monte Carlo [LINK]
• Monte Carlo Integrals [LINK]

multiple varying intercept

• Prosocial Chimps [LINK]

multivariate normal

• The idea behind the GP [LINK]
• Gaussian Processes and 'Non-parametric' Bayes [LINK]
• Correlations [LINK]
• Inference for GPs [LINK]
• Geographic Correlation and Oceanic Tools [LINK]

neural network

• MLP as universal approximator [LINK]

non-centered hierarchical model

• Gelman Schools and Hierarchical Pathology [LINK]
• Gelman Schools Theory for Topics about Restaurants [LINK]
• L, epsilon, and other tweaking [LINK]

noninformative prior

• Priors [LINK]

normal distribution

• The EM algorithm [LINK]
• Entropy [LINK]
• The idea behind the GP [LINK]
• Gaussian Processes and 'Non-parametric' Bayes [LINK]
• Sampling and the Central Limit Theorem [LINK]
• Variational Inference [LINK]
• ADVI [LINK]
• Distributions example - elections [LINK]
• Gaussian Mixture Model with ADVI [LINK]
• Inference for GPs [LINK]
• Geographic Correlation and Oceanic Tools [LINK]
• The Inverse Transform [LINK]
• Types of learning and MCMC [LINK]
• Mixtures and MCMC [LINK]
• Monte Carlo Integrals [LINK]

normal-normal model

• Bayesian Statistics [LINK]
• Sampling with $\sigma$ [LINK]
• Bayesian Regression [LINK]
• Gelman Schools and Hierarchical Pathology [LINK]
• Gelman Schools Theory for Topics about Restaurants [LINK]
• The normal model [LINK]
• The normal model with pymc [LINK]
• Priors [LINK]
• From the normal model to regression [LINK]

normalization

• Wrongly combining rejection with sampling [LINK]

nuts

• Exploring Hamiltonian Monte Carlo [LINK]
• L, epsilon, and other tweaking [LINK]

oceanic tools

• Poisson Regression - tools on islands part 2 [LINK]
• Geographic Correlation and Oceanic Tools [LINK]

optimization

• Variational Inference [LINK]
• ADVI [LINK]
• Gradient Descent and SGD [LINK]
• Calculus for optimization [LINK]
• Simulated Annealing [LINK]
• Utility or Risk - Back to optimization [LINK]

out-of-sample error

• Learning a model [LINK]
• Noisy Learning [LINK]
• Learning bounds and the Test set [LINK]
• Why do we need validation? [LINK]

overdispersion

• Poisson Regression - tools on islands part 2 [LINK]

parametric model

• Maximum Likelihood Estimation [LINK]
• Maximum Likelihood Estimation [LINK]

partial pooling

• Hierarchical Models [LINK]

pdf

• Distributions [LINK]

plug-in approximation

• The beta-binomial model of globe-tossing [LINK]

pmf

• Distributions [LINK]

poisson distribution

• Poisson Regression - tools on islands part 2 [LINK]
• Sampling from a discrete distribution [LINK]
• Sufficient Statistics and Exchangeability [LINK]

poisson regression

• Poisson Regression - tools on islands part 2 [LINK]
• Geographic Correlation and Oceanic Tools [LINK]
• Generalized Linear Models [LINK]
• Poisson and 0-inflated [LINK]
• Bayesian Workflow in the 0-inflated model [LINK]

poisson-gamma

• Sufficient Statistics and Exchangeability [LINK]

posterior

• The idea behind the GP [LINK]
• Gaussian Processes and 'Non-parametric' Bayes [LINK]
• Box's Loop [LINK]
• Inference for GPs [LINK]
• Geographic Correlation and Oceanic Tools [LINK]

posterior predictive

• The idea behind the GP [LINK]
• Gaussian Processes and 'Non-parametric' Bayes [LINK]
• Inference for GPs [LINK]
• Geographic Correlation and Oceanic Tools [LINK]
• Prosocial Chimps [LINK]
• Model checking [LINK]

priors

• The idea behind the GP [LINK]
• Gaussian Processes and 'Non-parametric' Bayes [LINK]
• Inference for GPs [LINK]
• Regression with custom priors [LINK]

probabilistic modeling

• Divergence and Deviance [LINK]
• Box's Loop [LINK]
• The Idea of Hamiltonian Monte Carlo [LINK]

probability

• Expectations and the Law of Large Numbers [LINK]
• Probability [LINK]

probability rules

• Probability [LINK]

proposal

• Simulated Annealing [LINK]

proposal matrix

• Sampling from a discrete distribution [LINK]

pymc3

• Inference for GPs [LINK]
• Marginalizing over Discretes [LINK]
• Types of learning and MCMC [LINK]
• Mixtures and MCMC [LINK]
• The normal model with pymc [LINK]
• From the normal model to regression [LINK]

pymc3 potentials

• Types of learning and MCMC [LINK]
• Mixtures and MCMC [LINK]

pytorch

• MLP as universal approximator [LINK]
• Lab 3 - Pytorch [LINK]

random variables

• Probability [LINK]

rat tumors

• Hierarchical Models [LINK]

regression

• The idea behind the GP [LINK]
• Gaussian Processes and 'Non-parametric' Bayes [LINK]
• Poisson Regression - tools on islands part 2 [LINK]
• Bayesian Regression [LINK]
• Lab 7 - Bayesian inference with PyMC3. [LINK]
• Inference for GPs [LINK]
• Generalized Linear Models [LINK]
• Poisson and 0-inflated [LINK]
• Bayesian Workflow in the 0-inflated model [LINK]
• From the normal model to regression [LINK]
• Regression with custom priors [LINK]

regularization

• Regularization [LINK]
• Understanding AIC [LINK]

rejection sampling

• Wrongly combining rejection with sampling [LINK]
• Basic Monte Carlo [LINK]
• Rejection Sampling [LINK]

rejection sampling on steroids

• Rejection Sampling [LINK]

representer theorem

• Gaussian Processes and 'Non-parametric' Bayes [LINK]

ridge

• Regularization [LINK]
• Unidentifiability, the ridge, and the lasso [LINK]

sampling

• Wrongly combining rejection with sampling [LINK]
• Sampling and the Central Limit Theorem [LINK]
• Basic Monte Carlo [LINK]
• Importance Sampling [LINK]
• The Inverse Transform [LINK]
• Rejection Sampling [LINK]

sampling and priors

• Priors [LINK]

sampling as marginalization

• The beta-binomial model of globe-tossing [LINK]

sampling distribution

• Sampling and the Central Limit Theorem [LINK]
• Frequentist Stats [LINK]
• Gelman Schools and Hierarchical Pathology [LINK]
• Gelman Schools Theory for Topics about Restaurants [LINK]
• Learning a model [LINK]
• Noisy Learning [LINK]

sampling distribution of variance

• Sampling and the Central Limit Theorem [LINK]

semi-supervised learning

• Types of learning and MCMC [LINK]
• Mixtures and MCMC [LINK]
• Mixture Models, and types of learning [LINK]

sgd

• ADVI [LINK]
• MLP as universal approximator [LINK]
• Gradient Descent and SGD [LINK]
• Lab 3 - Pytorch [LINK]

simulated annealing

• Simulated Annealing [LINK]
• From Annealing To Metropolis [LINK]
• Simulated Annealing [LINK]

slice sampling

• Slice Sampling [LINK]

standard error

• Sampling and the Central Limit Theorem [LINK]

stationarity

• Markov Chains and MCMC [LINK]
• From Annealing To Metropolis [LINK]

statistical mechanics

• Entropy [LINK]
• Exploring Hamiltonian Monte Carlo [LINK]
• L, epsilon, and other tweaking [LINK]
• Simulated Annealing [LINK]

step-size

• Convergence and Coverage [LINK]
• L, epsilon, and other tweaking [LINK]

stochastic noise

• Noisy Learning [LINK]

stratification

• Stratification for Variance Reduction [LINK]
• Stratification Example [LINK]

sufficient statistics

• Sufficient Statistics and Exchangeability [LINK]

supervised learning

• Classification Risk [LINK]
• Machine learning- ERM and Bayesian [LINK]
• Types of learning and MCMC [LINK]
• Mixtures and MCMC [LINK]
• Mixture Models, and types of learning [LINK]

switchpoint

• Imputation and Convergence [LINK]

temperature

• Simulated Annealing [LINK]
• Simulated Annealing [LINK]

test error

• Learning bounds and the Test set [LINK]
• Why do we need validation? [LINK]

test statistic

• Model checking [LINK]

testing set

• Learning bounds and the Test set [LINK]
• Why do we need validation? [LINK]

training error

• Learning a model [LINK]
• Noisy Learning [LINK]
• Learning bounds and the Test set [LINK]
• Why do we need validation? [LINK]

training set

• The EM algorithm [LINK]
• Learning bounds and the Test set [LINK]
• Why do we need validation? [LINK]

transition distribution

• Exploring Hamiltonian Monte Carlo [LINK]
• L, epsilon, and other tweaking [LINK]

transition matrix

• Intro to Gibbs Sampling [LINK]
• Markov Chains and MCMC [LINK]
• Metropolis-Hastings [LINK]

travelling salesman problem

• Simulated Annealing [LINK]

true-belief model

• Utility or Risk - Back to optimization [LINK]

type-2 mle

• Inference for GPs [LINK]
• Hierarchical Models [LINK]

uniform distribution

• Distributions example - elections [LINK]
• Distributions [LINK]
• Monte Carlo Integrals [LINK]

uninformative priors

• Regression with custom priors [LINK]

unsupervised learning

• Types of learning and MCMC [LINK]
• Mixtures and MCMC [LINK]
• Mixture Models, and types of learning [LINK]

validation error

• Why do we need validation? [LINK]

variance

• Noisy Learning [LINK]

variance reduction

• Stratification for Variance Reduction [LINK]
• Stratification Example [LINK]

variational inference

• Variational Inference [LINK]
• ADVI [LINK]

varying intercept

• Poisson Regression - tools on islands part 2 [LINK]
• Prosocial Chimps [LINK]

waic

• Poisson Regression - tools on islands part 2 [LINK]
• Model Comparison continued [LINK]
• Model Comparison [LINK]

weakly informative prior

• Priors [LINK]

x-likelihood

• The EM algorithm [LINK]

z-posterior

• The EM algorithm [LINK]

zero-inflated

• Poisson and 0-inflated [LINK]
• Bayesian Workflow in the 0-inflated model [LINK]