# 12. Glossary#

Autocorrelation: Autocorrelation is the correlation of a signal with a lagged copy of itself. Conceptually, you can think of it as how similar observations are as a function of the time lag between them. Large autocorrelation is a concern in MCMC samples as it reduces the effective sample size.

Aleatoric Uncertainty: Aleatoric uncertainty is related to the notion that there are some quantities that affect a measurement or observation that are intrinsically unknowable or random. For example, even if we were able to exactly replicate condition such as direction, altitude and force when shooting an arrow with a bow. The arrow will still not hit the same point, because there are other conditions that we do not control like fluctuations of the atmosphere or vibrations of the arrow shaft, that are random.

Bayesian Inference: Bayesian Inference is a particular form of statistical inference based on combining probability distributions in order to obtain other probability distributions. In other words is the formulation and computation of conditional probability or probability densities, $$p(\boldsymbol{\theta} \mid \boldsymbol{Y}) \propto p(\boldsymbol{Y} \mid \boldsymbol{\theta}) p(\boldsymbol{\theta})$$.

Bayesian workflow: Designing a good enough model for a given problem requires significant statistical and domain knowledge expertise. Such design is typically carried out through an iterative process called Bayesian workflow. This process includes the three steps of model building [17]: inference, model checking/improvement, and model comparison. In this context the purpose of model comparison is not necessarily restricted to pick the best model, but more importantly to better understand the models.

Causal inference: or Observational causal inference. The procedures and tools used to estimate the impact of a treatment (or intervention) in some system without testing the intervention. That is from observational data instead of experimental data.

Covariance Matrix and Precision Matrix: The covariance matrix is a square matrix that contains the covariance between each pair of elements of a collection of random variable. The diagonal of the covariance matrix is the variance of the random variable. The precision matrix is the matrix inverse of the covariance matrix.

Design Matrix: In the context of regression analysis a design matrix is a matrix of values of the explanatory variables. Each row represents an individual object, with the successive columns corresponding to the variables and their specific values for that observation. It can contain indicator variables (ones and zeros) indicating group membership, or it can contain continuous values.

Decision tree: A decision tree is a flowchart-like structure in which each internal node represents a “test” on an attribute (e.g. whether a coin flip comes up heads or tails), each branch represents the outcome of the test, and each leaf node represents a class label (decision taken after computing all attributes). The paths from root to leaf represent classification rules. The values at the leaf nodes can be continuous if the tree is used for regression.

dse: The standard error of component-wise differences of elpd_loo between two models. This error is smaller than the standard error (se in az.compare) for individual models. The reason being that generally some observations are as easy/hard to predict for all models and thus this introduce correlations.

d_loo: The difference in elpd_loo for two models. If more than two models are compared, the difference is computed relative to the model with highest elpd_loo).

Epistimic Uncertainty: Epistemic uncertainty is related to the lack of knowledge of the states of a system by some observer. It is related to the knowledge that we could have in principle but not in practice and not about the intrinsic unknowable quantity of nature (contrast with aleatory uncertainty). For example, we may be uncertain of the weight of an item because we do not have an scale at hand, so we estimate the weight by lifting it, or we may have one scale but with a precision limited to the kilogram. We could also have epistemic uncertainty if we design an experiment or perform a computation ignoring factors. For example, to estimate how much time we will have to drive to another city, we may omit the time spent at tolls, or we may assume excellent weather or road conditions etc. In other words, epistemic uncertainty is about ignorance and in opposition to aleatoric, uncertainty, we can in principle reduce it by obtaining more information.

Statistic: A statistic (not plural) or sample statistic is any quantity computed from a sample. Sample statistics are computed for several reasons including estimating a population (or data generating process) parameter, describing a sample, or evaluating a hypothesis. The sample mean (also known as empirical mean) is a statistic, the sample variance (or empirical variance) is another example. When a statistic is used to estimate a population (or data generating process) parameter, the statistic is called an estimator. Thus, the sample mean can be an estimator and the posterior mean can be another estimator.

ELPD: Expected Log-pointwise Predictive Density (or expected log pointwise predictive probabilities for discrete model). This quantity is generally estimated by cross-validation or using methods such as WAIC (elpd_waic) or LOO (elpd_loo). As probability densities can be smaller or larger than 1, the ELPD can be negative or positive for continuous variables and non-negative for discrete variables.

Exchangeability: A sequence of Random variables is exchangeable if their joint probability distribution does not change when the positions in the sequence is altered. Exchangeable random variables are not necessarily iid, but iid are exchangeable.

Exploratory Analysis of Bayesian Models: The collection of tasks necessary to perform a successful Bayesian data analysis that are not the inference itself. This includes. Diagnosing the quality of the inference results obtained using numerical methods. Model criticism, including evaluations of both model assumptions and model predictions. Comparison of models, including model selection or model averaging. Preparation of the results for a particular audience.

Hamiltonian Monte Carlo Hamiltonian Monte Carlo (HMC) is a Markov chain Monte Carlo (MCMC) method that uses the gradient to efficiently explore a probability distribution function. In Bayesian statistics this is most commonly used to obtain samples from the posterior distribution. HMC methods are instances of the Metropolis–Hastings algorithm, where the proposed new points are computed from a Hamiltonian, this allows the methods to proposed new states to be far from the current one with high acceptance probability. The evolution of the system is simulated using a time-reversible and volume-preserving numerical integrator (most commonly the leapfrog integrator). The efficiency of the HMC method is highly dependant on certain hyperparameters of the method. Thus, the most useful methods in Bayesian statistics are adaptive dynamics versions of HMC that can adjust those hyperparameters automatically during the warm-up or tuning phase.

Heteroscedasticity: A sequence of random variables is heteroscedastic if its random variables do not have the same variance, i.e. if they are not homoscedastic. This is also known as heterogeneity of variance.

Homoscedasticity: A sequence of random variables is homoscedastic if all its random variables have the same finite variance. This is also known as homogeneity of variance. The complementary notion is called heteroscedasticity.

iid: Independent and identically distributed. A collection of random variables is independent and identically distributed if each random variable has the same probability distribution as the others and all are mutually independent. If a collection of random variables is iid it is also exchangeable, but the converse is not necessarily true.

Individual Conditional Expectation ICE: An ICE shows the dependence between the response variable and a covariate of interest. This is done for each sample separately with one line per sample. This contrast to PDPs where the average effect of the covariate is represented.

Inference: Colloquially, inference is reaching a conclusion based on evidence and reasoning. In this book refer to inference we generally mean about Bayesian Inference, which has a more restricted and precise definition. Bayesian Inference is the process of conditioning models to the available data and obtaining posterior distributions. Thus, in order to reach a conclusion based on evidence and reasoning, we need to perform more steps that mere Bayesian inference. Hence the importance of discussing Bayesian analysis in terms of exploratory analysis of Bayesian models or more generally in term of Bayesian workflows.

Imputation: Replacing missing data values through a method of choice. Common methods may include most common occurrence or interpolation based on other (present) observed data.

KDE: Kernel Density Estimation. A non-parametric method to estimate the probability density function of a random variable from a finite set of samples. We often use the term KDE to talk about the estimated density and not the method.

LOO: Short for Pareto smoothed importance sampling leave one out cross-validation (PSIS-LOO-CV). In the literature “LOO” may be restricted to leave one out cross-validation.

Maximum a Posteriori (MAP) An estimator of an unknown quantity, that equals the mode of the posterior distribution. The MAP estimator requires optimization of the posterior, unlike the posterior mean which requires integration. If the priors are flat, or in the limit of infinite sample size, the MAP estimator is equivalent to the Maximum Likelihood estimator.

Odds A measure of the likelihood of a particular outcome. They are calculated as the ratio of the number of events that produce that outcome to the number that do not. Odds are commonly used in gambling.

Overfitting: A model overfits when produces predictions too closely to the dataset used for fitting the model failing to fit new datasets. In terms of the number of parameters an overfitted model contains more parameters than can be justified by the data. An arbitrary over-complex model will fit not only the data but also the noise, leading to poor predictions.

Partial Dependence Plots PDP: A PDP shows the dependence between the response variable and a set of covariates of interest, this is done by marginalizing over the values of all other covariates. Intuitively, we can interpret the partial dependence as the expected value of the response variable as function of the covariates of interest.

Pareto k estimates $$\hat k$$: A diagnostic for Pareto smoothed importance sampling (PSIS), which is used by LOO. The Pareto k diagnostic estimates how far an individual leave-one-out observation is from the full distribution. If leaving out an observation changes the posterior too much then importance sampling is not able to give reliable estimates. If $$\hat \kappa < 0.5$$, then the corresponding component of elpd_loo is estimated with high accuracy. If $$0.5< \hat \kappa <0.7$$ the accuracy is lower, but still useful in practice. If $$\hat \kappa > 0.7$$, then importance sampling is not able to provide a useful estimate for that observation. The $$\hat \kappa$$ values are also useful as a measure of influence of an observation. Highly influential observations have high $$\hat \kappa$$ values. Very high $$\hat \kappa$$ values often indicate model misspecification, outliers, or mistakes in the data processing.

Point estimate A single value, generally but not necessarily in parameter space, used as a summary of best estimate of an unknown quantity. A point estimate can be contrasted with an interval estimate like highest density intervals, which provides a range or interval of values describing the unknown quantity. We can also contrast a point estimate with distributional estimates, like the posterior distribution or its marginals.

p_loo: The difference between elpd_loo: and the non-cross-validated log posterior predictive density. It describes how much more difficult it is to predict future data than the observed data. Asymptotically under certain regularity conditions, p_loo can be interpreted as the effective number of parameters. In well behaving cases p_loo should be lower than the number of parameters in the model and smaller than the number observations in the data. If not, this is an indication that the model has very weak predictive capability and may thus indicate a severe model misspecification. See high Pareto k diagnostic values.

Probabilistic Programming Language: A programming syntax composed of primitives that allows one to define Bayesian models and perform inference automatically. Typically a Probabilistic Programming Language also includes functionality to generate prior or posterior predictive samples or even to analysis result from inference.

Prior predictive distribution: The expected distribution of the data according to the model (prior and likelihood). That is, the data the model is expecting to see before seeing any data. See Equation (1.7). The prior predictive distribution can be used for prior elicitation, as it is generally easier to think in terms of the observed data, than to think in terms of model parameters.

Posterior predictive distribution: This is the distribution of (future) data according to the posterior, which in turn is a consequence of the model (prior and likelihood) and observed data. In other words, these are the model’s predictions. See Equation (1.8). Besides generating predictions, the posterior predictive distribution can be used to asses the model fit, by comparing it with the observed data.

Residuals: The difference between an observed value and the estimated value of the quantity of interest. If a model assumes that the variance is finite and the same for all residuals, we say we have homoscedasticity. If instead the variance can change, we say we have heteroscedasticity.

Sufficient statistics: A statistic is sufficient with respect to a model parameter if no other statistic computed from the same sample provides any additional information about that sample. In other words, that statistic is sufficient to summarize your samples without losing information. For example, given a sample of independent values from a normal distribution with expected value $$\mu$$ and known finite variance the sample mean is sufficient statistics for $$\mu$$. Notice that the mean says nothing about the dispersion, thus it is only sufficient with respect to the parameter $$\mu$$. It is known that for iid data the only distributions with a sufficient statistic with dimension equal to the dimension of $$\theta$$ are the distributions from the exponential family. For other distribution, the dimension of the sufficient statistic increases with the sample size.

Synthetic data: Also known as fake data it refers to data generated from a model instead of being gathered from experimentation or observation. Samples from the posterior/prior predictive distributions are examples of synthetic data.

Timestamp: A timestamp is an encoded information to identify when a certain event happens. Usually a timestamp is written in the format of date and time of day, with more accurate fraction of a second when necessary.

Turing-complete In colloquial usage, is used to mean that any real-world general-purpose computer or computer language can approximately simulate the computational aspects of any other real-world general-purpose computer or computer language.