Канал: Ben Lambert

Online conference at Oxford University: Inference for expensive systems in mathematical biology

Online conference at Oxford University: Inference for expensive systems in mathematical biology

Conclusions and references for grammar of graphics

Conclusions and references for grammar of graphics

The path to a good visualisation using grammar of graphics

The path to a good visualisation using grammar of graphics

Aesthetics and geoms: biological analogy

Aesthetics and geoms: biological analogy

Introducing aesthetics and geoms

Introducing aesthetics and geoms

Comparing traditional versus grammar of graphics approaches to graphing

Comparing traditional versus grammar of graphics approaches to graphing

Introduction to grammar of graphics short course

Introduction to grammar of graphics short course

Centered versus non-centered hierarchical models

Centered versus non-centered hierarchical models

The distribution zoo app to help to understand and use probability distributions

The distribution zoo app to help to understand and use probability distributions

How to code up a model with discrete parameters in Stan

How to code up a model with discrete parameters in Stan

How to write your first Stan program

How to write your first Stan program

How to code up a bespoke probability density in Stan

How to code up a bespoke probability density in Stan

What are divergent iterations and what to do about them?

What are divergent iterations and what to do about them?

Introducing Bayes factors and marginal likelihoods

Introducing Bayes factors and marginal likelihoods

Bob’s bees: the importance of using multiple bees (chains) to judge MCMC convergence

Bob’s bees: the importance of using multiple bees (chains) to judge MCMC convergence

Using a Bayes box to calculate the denominator

Using a Bayes box to calculate the denominator

An introduction to discrete conditional probability distributions.

An introduction to discrete conditional probability distributions.

An introduction to continuous conditional probability distributions

An introduction to continuous conditional probability distributions

The importance of step size for Random Walk Metropolis

The importance of step size for Random Walk Metropolis

Estimating the posterior predictive distribution by sampling

Estimating the posterior predictive distribution by sampling

Explaining the intuition behind Bayesian inference

Explaining the intuition behind Bayesian inference

Using the Random Walk Metropolis algorithm to sample from a cow surface distribution

Using the Random Walk Metropolis algorithm to sample from a cow surface distribution

How to do integration by sampling

How to do integration by sampling

Why is it difficult to calculate the denominator of Bayes’ rule in practice?

Why is it difficult to calculate the denominator of Bayes’ rule in practice?

How to derive a Gibbs sampling routine in general

How to derive a Gibbs sampling routine in general

Why we typically use dependent sampling to sample from the posterior

Why we typically use dependent sampling to sample from the posterior

An introduction to Jeffreys priors - 3

An introduction to Jeffreys priors - 3

An introduction to numerical integration through Gaussian quadrature

An introduction to numerical integration through Gaussian quadrature

Explaining the Kullback-Liebler divergence through secret codes

Explaining the Kullback-Liebler divergence through secret codes

The ideal measure of a model's predictive fit

The ideal measure of a model's predictive fit

An introduction to importance sampling

An introduction to importance sampling

An introduction to inverse transform sampling

An introduction to inverse transform sampling

Explaining the difference between confidence and credible intervals

Explaining the difference between confidence and credible intervals

What is the difference between independent and dependent sampling algorithms?

What is the difference between independent and dependent sampling algorithms?

The difficulty with real life Bayesian inference: high multidimensional integrals (and sums)

The difficulty with real life Bayesian inference: high multidimensional integrals (and sums)

The problems with using simple Monte Carlo to determine the marginal likelihood

The problems with using simple Monte Carlo to determine the marginal likelihood

Maximum likelihood estimation for the beer example model

Maximum likelihood estimation for the beer example model

An introduction to discrete probability distributions

An introduction to discrete probability distributions

What is meant by independent sampling and how can it be used to understand a distribution?

What is meant by independent sampling and how can it be used to understand a distribution?

What is meant by entropy in statistics?

What is meant by entropy in statistics?

The illusion of uninformative priors

The illusion of uninformative priors

The problem with discrete approximation to integrals or probability densities

The problem with discrete approximation to integrals or probability densities

An introduction to Gibbs sampling

An introduction to Gibbs sampling

An introduction to importance sampling - optimal importance distributions

An introduction to importance sampling - optimal importance distributions

Example likelihood model: waiting times between beer orders

Example likelihood model: waiting times between beer orders

An introduction to Jeffreys priors - 2

An introduction to Jeffreys priors - 2

An introduction to Jeffreys priors - 1

An introduction to Jeffreys priors - 1

The intuition behind the Hamiltonian Monte Carlo algorithm

The intuition behind the Hamiltonian Monte Carlo algorithm

The duality of meaning for likelihoods and probability distributions: the equivalence principle

The duality of meaning for likelihoods and probability distributions: the equivalence principle

An example of how an improper prior leads to an improper posterior

An example of how an improper prior leads to an improper posterior

An introduction to continuous probability distributions.

An introduction to continuous probability distributions.

Two-dimensional continuous distributions: an introduction

Two-dimensional continuous distributions: an introduction

An introduction to the Bernoulli and binomial distributions

An introduction to the Bernoulli and binomial distributions

What does it mean to sample from a distribution?

What does it mean to sample from a distribution?

An introduction to the Poisson distribution - 2

An introduction to the Poisson distribution - 2

What is meant by overfitting?

What is meant by overfitting?

Effective sample size: representing the cost of dependent sampling

Effective sample size: representing the cost of dependent sampling

An introduction to the Beta distribution

An introduction to the Beta distribution

An introduction to discrete marginal probability distributions

An introduction to discrete marginal probability distributions

What is a conjugate prior?

What is a conjugate prior?

On the sensitivity of the marginal likelihood to prior choice

On the sensitivity of the marginal likelihood to prior choice

Random variables and probability distributions.

Random variables and probability distributions.

Two-dimensional discrete distributions: an introduction

Two-dimensional discrete distributions: an introduction

An introduction to the concept of a sufficient statistic

An introduction to the concept of a sufficient statistic

Breast cancer example use of Bayes' rule - 2

Breast cancer example use of Bayes' rule - 2

Using bees to demonstrate the importance of overdispersed Markov chains in MCMC

Using bees to demonstrate the importance of overdispersed Markov chains in MCMC

An introduction to continuous marginal probability distributions

An introduction to continuous marginal probability distributions

An introduction to the Poisson distribution - 1

An introduction to the Poisson distribution - 1

How to use rejection sampling to uniformly sample within a cow's boundaries

How to use rejection sampling to uniformly sample within a cow's boundaries

Why is a likelihood not a probability distribution?

Why is a likelihood not a probability distribution?

What is meant by overfitting?

What is meant by overfitting?

An introduction to the Random Walk Metropolis algorithm

An introduction to the Random Walk Metropolis algorithm

An introduction to mutual information

An introduction to mutual information

Breast cancer example use of Bayes' rule - 1

Breast cancer example use of Bayes' rule - 1

An introduction to rejection sampling

An introduction to rejection sampling

An introduction to reference priors

An introduction to reference priors

An introduction to central limit theorems

An introduction to central limit theorems

Constrained parameters? Use Metropolis-Hastings

Constrained parameters? Use Metropolis-Hastings

What is a posterior predictive check and why is it useful?

What is a posterior predictive check and why is it useful?

An introduction to the gamma distribution

An introduction to the gamma distribution

Evaluating model fit through AIC, DIC, WAIC and LOO-CV

Evaluating model fit through AIC, DIC, WAIC and LOO-CV

The syllabus covered by the book and YouTube course

The syllabus covered by the book and YouTube course

Cauchy Schwarz Inequality Proof part 2

Cauchy Schwarz Inequality Proof part 2

Finite sample properties of Wald + Score and Likelihood Ratio test statistics

Finite sample properties of Wald + Score and Likelihood Ratio test statistics

Cauchy Schwarz Inequality Proof new

Cauchy Schwarz Inequality Proof new

Econometric model building - general to specific

Econometric model building - general to specific

Bayesian posterior sampling

Bayesian posterior sampling

Bayesian statistics syllabus

Bayesian statistics syllabus

Serial correlation - The Durbin-Watson test

Serial correlation - The Durbin-Watson test

Moving Average processes - Stationary and Weakly Dependent

Moving Average processes - Stationary and Weakly Dependent

Factor Analysis - model representation - part 2

Factor Analysis - model representation - part 2

Factor Analysis - an introduction

Factor Analysis - an introduction

Model implied variance-covariance matrix of indicators (matrix form) - part 1

Model implied variance-covariance matrix of indicators (matrix form) - part 1

Variance-covariance matrix using matrix notation of factor analysis

Variance-covariance matrix using matrix notation of factor analysis

Factor Analysis - model representation

Factor Analysis - model representation

Covariance between indicators and factors

Covariance between indicators and factors

Factor Analysis - model representation - part 4 (matrix form)

Factor Analysis - model representation - part 4 (matrix form)

Factor analysis assumptions

Factor analysis assumptions

Model implied variance-covariance matrix - an example

Model implied variance-covariance matrix - an example

Factor Analysis - model representation - part 3 (matrix form)

Factor Analysis - model representation - part 3 (matrix form)

Model implied variance-covariance matrix of indicators (matrix form) - part 2

Model implied variance-covariance matrix of indicators (matrix form) - part 2

Maximum likelihood estimation of factor analysis models - part 1

Maximum likelihood estimation of factor analysis models - part 1

Maximum likelihood estimation of factor analysis models - fitting function

Maximum likelihood estimation of factor analysis models - fitting function

Derivation of variance-covariance matrix in factor analysis - part 2

Derivation of variance-covariance matrix in factor analysis - part 2

Derivation of variance-covariance matrix in factor analysis - part 1

Derivation of variance-covariance matrix in factor analysis - part 1

Maximum likelihood estimation of factor analysis models - part 2

Maximum likelihood estimation of factor analysis models - part 2

Derivation of variance-covariance matrix in factor analysis - part 3

Derivation of variance-covariance matrix in factor analysis - part 3

Maximum Likelihood - Cramer Rao Lower Bound Intuition

Maximum Likelihood - Cramer Rao Lower Bound Intuition

Factor analysis: predicted variance and covariance of indicators - part 2

Factor analysis: predicted variance and covariance of indicators - part 2

Wald Test - introduction

Wald Test - introduction

Factor analysis: predicted variance and covariance of indicators - part 1

Factor analysis: predicted variance and covariance of indicators - part 1

Score test (Lagrange Multiplier test) - introduction

Score test (Lagrange Multiplier test) - introduction

Likelihood ratio test - introduction

Likelihood ratio test - introduction

Conditional Independence Assumption for Continuous Variables

Conditional Independence Assumption for Continuous Variables

Selection bias as viewed as a problem with samples

Selection bias as viewed as a problem with samples

Linear regression and causality

Linear regression and causality

Propensity score - introduction and theorem

Propensity score - introduction and theorem

Sample balancing via stratification and matching

Sample balancing via stratification and matching

Propensity score theorem proof - part 1

Propensity score theorem proof - part 1

The Law of Iterated Expectations: an introduction

The Law of Iterated Expectations: an introduction

The Law of Iterated Expectations: introduction to nested form

The Law of Iterated Expectations: introduction to nested form

Propensity score matching - mathematics behind estimation

Propensity score matching - mathematics behind estimation

Propensity score theorem proof - part 2

Propensity score theorem proof - part 2

Propensity score matching: an introduction

Propensity score matching: an introduction

Causation in econometrics - selection bias and average causal effect

Causation in econometrics - selection bias and average causal effect

Random assignment - removes selection bias

Random assignment - removes selection bias

Causation in econometrics - a simple comparison of group means

Causation in econometrics - a simple comparison of group means

The Rubin Causal model - an introduction

The Rubin Causal model - an introduction

Causality - an introduction

Causality - an introduction

The conditional independence assumption - example

The conditional independence assumption - example

The conditional independence assumption - intuition

The conditional independence assumption - intuition

How to check if treatment is randomly assigned?

How to check if treatment is randomly assigned?

The conditional independence assumption: introduction

The conditional independence assumption: introduction

The average causal effect - an example

The average causal effect - an example

The average causal effect with continuous treatment variables

The average causal effect with continuous treatment variables

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Fixed Effects, First Differences and Pooled OLS - intuition

Fixed Effects, First Differences and Pooled OLS - intuition

The Phillips-Perron test for a unit root - an introduction

The Phillips-Perron test for a unit root - an introduction

Fixed Effects estimators: an introduction

Fixed Effects estimators: an introduction

Representing homoscedasticity and no autocorrelation in matrix form - part 2

Representing homoscedasticity and no autocorrelation in matrix form - part 2

Representing heteroscedasticity in matrix form

Representing heteroscedasticity in matrix form

Representing homoscedasticity and no autocorrelation in matrix form - part 1

Representing homoscedasticity and no autocorrelation in matrix form - part 1

BLUE estimators in presence of heteroscedasticity - GLS - part 1

BLUE estimators in presence of heteroscedasticity - GLS - part 1

GLS estimators in matrix form - part 1

GLS estimators in matrix form - part 1

BLUE estimators in presence of heteroscedasticity - GLS - part 2

BLUE estimators in presence of heteroscedasticity - GLS - part 2

GLS estimators in matrix form - part 3

GLS estimators in matrix form - part 3

The variance of GLS estimators

The variance of GLS estimators

GLS estimators in matrix form - part 2

GLS estimators in matrix form - part 2

GLS estimators in the presence of autocorrelation and heteroscedasticity in matrix form

GLS estimators in the presence of autocorrelation and heteroscedasticity in matrix form

The Kronecker Product of two matrices - an introduction

The Kronecker Product of two matrices - an introduction

GLS - example in matrix form

GLS - example in matrix form

SURE estimation - an introduction - part 2

SURE estimation - an introduction - part 2

SURE estimation - an introduction - part 1

SURE estimation - an introduction - part 1

SURE estimation - autocorrelation and heteroscedasticity

SURE estimation - autocorrelation and heteroscedasticity

Geometric Least Squares Column Space Intuition

Geometric Least Squares Column Space Intuition

Kronecker Matrix Product - properties

Kronecker Matrix Product - properties

SURE estimator - same independent variables - part 2

SURE estimator - same independent variables - part 2

SURE estimator - same independent variables - part 1

SURE estimator - same independent variables - part 1

SURE estimator derivation - part 1

SURE estimator derivation - part 1

SURE estimator derivation - part 2

SURE estimator derivation - part 2

SURE estimator - same independent variables - part 3

SURE estimator - same independent variables - part 3

Autocorrelation an introduction

Autocorrelation an introduction

Random walk not weakly dependent

Random walk not weakly dependent

One sample t test on mean

One sample t test on mean

Independent two sample t test for populations with equal variances

Independent two sample t test for populations with equal variances

Variance Inflation Factors: testing for multicollinearity

Variance Inflation Factors: testing for multicollinearity

Proof that the trace of Mx is p

Proof that the trace of Mx is p

Simultaneous Equation Models: Order condition for parameter identification

Simultaneous Equation Models: Order condition for parameter identification

Testing for significance of correlation

Testing for significance of correlation

Geometric intepretation of least squares - orthogonal projection

Geometric intepretation of least squares - orthogonal projection

Geometric Interpretation of Ordinary Least Squares: An Introduction

Geometric Interpretation of Ordinary Least Squares: An Introduction

Geometric Interpretation of Ordinary Least Squares: An Example

Geometric Interpretation of Ordinary Least Squares: An Example

Geometric interpretation of Least Squares: geometrical derivation of estimator

Geometric interpretation of Least Squares: geometrical derivation of estimator

Orthogonal Projection Operator in Least Squares - part 1

Orthogonal Projection Operator in Least Squares - part 1

Orthogonal Projection Operator in Least Squares - part 2

Orthogonal Projection Operator in Least Squares - part 2

Orthogonal Projection Operator in Least Squares - part 3

Orthogonal Projection Operator in Least Squares - part 3

Monte Carlo Simulation for Ordinary Least Squares

Monte Carlo Simulation for Ordinary Least Squares

Estimating the error variance in matrix form - part 2

Estimating the error variance in matrix form - part 2

Estimating the error variance in matrix form - part 1

Estimating the error variance in matrix form - part 1

Monte Carlo Simulation of Omitted Variable Bias in Least Squares

Monte Carlo Simulation of Omitted Variable Bias in Least Squares

Monte Carlo Simulation for estimators: An Introduction

Monte Carlo Simulation for estimators: An Introduction

Estimating the error variance in matrix form - part 3

Estimating the error variance in matrix form - part 3

Estimating the error variance in matrix form - part 4

Estimating the error variance in matrix form - part 4

Estimating the error variance in matrix form - part 5

Estimating the error variance in matrix form - part 5

Estimating the error variance in matrix form - part 6

Estimating the error variance in matrix form - part 6

Simultaneous equation models - reduced form and structural equations

Simultaneous equation models - reduced form and structural equations

Simultaneous equation models - an introduction

Simultaneous equation models - an introduction

Simultaneous equation models - parameter identification

Simultaneous equation models - parameter identification

Least squares comparison with Maximum Likelihood - proof that OLS is BUE

Least squares comparison with Maximum Likelihood - proof that OLS is BUE

Least Squares as a Maximum Likelihood estimator

Least Squares as a Maximum Likelihood estimator

Maximum Likelihood estimation of Logit and Probit

Maximum Likelihood estimation of Logit and Probit

Probit model as a result of a latent variable model

Probit model as a result of a latent variable model

Log odds interpretation of logistic regression

Log odds interpretation of logistic regression

Maximum Likelihood: Bernoulli random variables estimator variance part 2

Maximum Likelihood: Bernoulli random variables estimator variance part 2

Maximum likelihood: Normal error distribution - estimator variance part 3

Maximum likelihood: Normal error distribution - estimator variance part 3

Maximum likelihood: Normal error distribution - estimator variance part 1

Maximum likelihood: Normal error distribution - estimator variance part 1

Maximum likelihood: Normal error distribution - estimator variance part 2

Maximum likelihood: Normal error distribution - estimator variance part 2

Maximum Likelihood: Bernoulli random variables estimator variance part 1

Maximum Likelihood: Bernoulli random variables estimator variance part 1

Introduction to the matrix formulation of econometrics

Introduction to the matrix formulation of econometrics