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  2. Dependent and independent variables - Wikipedia

    en.wikipedia.org/wiki/Dependent_and_independent...

    It is possible to have multiple independent variables or multiple dependent variables. For instance, in multivariable calculus, one often encounters functions of the form z = f(x,y), where z is a dependent variable and x and y are independent variables. [8] Functions with multiple outputs are often referred to as vector-valued functions.

  3. Graphical model - Wikipedia

    en.wikipedia.org/wiki/Graphical_model

    An example of a directed, cyclic graphical model. Each arrow indicates a dependency. In this example: D depends on A, B, and C; and C depends on B and D; whereas A and B are each independent. The next figure depicts a graphical model with a cycle. This may be interpreted in terms of each variable 'depending' on the values of its parents in some ...

  4. Causal graph - Wikipedia

    en.wikipedia.org/wiki/Causal_graph

    Figure 1 is a causal graph that represents this model specification. Each variable in the model has a corresponding node or vertex in the graph. Additionally, for each equation, arrows are drawn from the independent variables to the dependent variables. These arrows reflect the direction of causation.

  5. Dependency network (graphical model) - Wikipedia

    en.wikipedia.org/wiki/Dependency_Network...

    A consistent dependency network for a set of random variables = (, …,) with joint distribution () is a pair (,) where is a cyclic directed graph, where each of its nodes corresponds to a variable in , and is a set of conditional probability distributions.

  6. Dependency graph - Wikipedia

    en.wikipedia.org/wiki/Dependency_graph

    In mathematics, computer science and digital electronics, a dependency graph is a directed graph representing dependencies of several objects towards each other. It is possible to derive an evaluation order or the absence of an evaluation order that respects the given dependencies from the dependency graph.

  7. Regression analysis - Wikipedia

    en.wikipedia.org/wiki/Regression_analysis

    In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the outcome or response variable, or a label in machine learning parlance) and one or more error-free independent variables (often called regressors, predictors, covariates, explanatory ...

  8. Simple linear regression - Wikipedia

    en.wikipedia.org/wiki/Simple_linear_regression

    Graph of points and linear least squares lines in the simple linear regression numerical example. The 0.975 quantile of Student's t-distribution with 13 degrees of freedom is t * 13 = 2.1604, and thus the 95% confidence intervals for α and β are

  9. Bayesian network - Wikipedia

    en.wikipedia.org/wiki/Bayesian_network

    A Bayesian network (also known as a Bayes network, Bayes net, belief network, or decision network) is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG). [1]