# Tags

A tag is a keyword or label that categorizes your question with other, similar questions. Using the right tags makes it easier for others to find and answer your question.

 Type to find tags:
 scikit-learn× 255 Machine learning framework for Python. hidden-markov-model× 255 used for modelling systems that are assumed to be Markov processes with hidden (i.e. unobserved) states. glmm× 254 Generalized Linear Mixed (effects) Models are typically used for modeling non-independent non-normal data (eg, longitudinal binary data). power× 250 Is a property of a hypothesis testing method: the probability of rejecting the null hypothesis given that it is false, i.e. the probability of not making a type II error. The power of a test depends o… reliability× 249 said to have a high reliability if it produces similar results under consistent conditions. DO NOT confuse reliability with validity (see tag wiki). kernel-trick× 249 used in machine learning to generalize linear techniques to nonlinear situations, especially SVMs, PCA, and GPs. Not to be confused with [kernel-smoothing], for kernel density estim… continuous-data× 249 called continuous if its set of possible values is uncountable, and the chance that it takes any particular value is zero ($\text{P}(X = x) = 0$ for every real number $x$). A … group-differences× 247 Group differences broadly refer to statistics which quantify the differences between two or more subpopulations. naive-bayes× 246 a simple probabilistic classifier based on applying Bayes' theorem with strong independence assumptions. A more descriptive term for the underlying probability model would … skewness× 243 Skewness measures (or refers to) a degree of asymmetry in the distribution of a variable. natural-language× 242 a set of techniques from linguistics, artificial intelligence, machine learning and statistics that aim at processing and understanding human languages. gaussian-process× 240 Gaussian processes refer to stochastic processes whose realization consists of normally distributed random variables, with the additional property that any finite collection of these random variables … posterior× 236 Refers to the probability distribution of parameters conditioned on data in Bayesian statistics. post-hoc× 235 decided upon after the data has been collected, as opposed to "a priori". bayes× 235 Combining probabilities with Bayes' Theorem, especially as used for conditional inference. quantiles× 234 The quantiles of a distribution refer to points on its cumulative distribution function. Some common quantiles are quartiles and percentiles. robust× 233 Robustness in general refers to a statistic's insensitivity to deviations from its underlying assumptions (Huber and Ronchetti, 2009). small-sample× 233 Refers to any statistical complication or problem due to having few data. kolmogorov-smirnov× 233 a test for goodness of fit of data to a distribution. It is often used to test whether a variable is normally distributed. standardization× 232 Shifting and rescaling data to assure zero mean and unit variance. biostatistics× 231 The science of statistics applied to the analysis of biological or medical data. autoregressive× 230 a stochastic process modelling time series, which specifies the value of the series linearly in terms of the previous values. discrete-data× 229 Refers to data generated from a distribution that has a countable sample space. Discrete data may be nominal (e.g. the distribution of race in a sample of individuals) or ordinal (e.g. the number of e… likelihood× 229 Given a random variable $X$ which arise from a parameterized distribution $F(X;θ)$, the likelihood is defined as the probability of observed data as a function of $θ: \text{L}(θ)=\text{P}(θ;X=x)$ distance× 228 Measure of distance between distributions or variables, such as Euclidean distance between points in n-space. large-data× 228 'Large data' refers to situations where the number of observations (data points) is so large that it necessitates changes in the way the data analyst thinks about or conducts the analysis. (Not to be … curve-fitting× 227 fit curves (as in linear or non-linear regression) to data. lognormal× 224 the distribution of a random variable whose logarithm has a normal distribution. causal-inference× 223 Causal inference tries to quantify the effect of a change in $X$ on $Y$ whilst holding constant or eliminating all other relevant factors which might influence this relationship. basic-concepts× 222 average× 221 Average most often refers to the arithmetic mean, but more generally to measures of central tendency that use most, or all, of the data values. Examples include trimmed mean, Winsorized mean, harmonic… exponential× 221 A distribution describing the time between events in a Poisson process; a continuous analogue of the geometric distribution. scales× 221 express measurements, usually ratio, interval, ordinal or nominal scales. median× 220 the value below which half the data or probability distribution lies - when the sample size is odd, the median is the 'middle' value of an ordered sample. covariance-matrix× 218 Given a random vector $\mathbf x$ with $k$ elements, its covariance matrix (also called variance-covariance matrix or simply variance matrix) is a $k \times k$ matrix $\mathbf C$ of covariances betwee… kernel-smoothing× 217 Kernel smoothing techniques, such as kernel density estimation (KDE) and Nadaraya-Watson kernel regression, estimate functions by local interpolation from data points. Not to be confused with [kernel-…