Concepts inAutomated probabilistic modeling for relational data
Scientific modelling
Scientific modelling is the process of generating abstract, conceptual, graphical or mathematical models. Science offers a growing collection of methods, techniques and theory about all kinds of specialized scientific modelling. A scientific model can provide a way to read elements easily which have been broken down to a simpler form. Modelling is an essential and inseparable part of all scientific activity, and many scientific disciplines have their own ideas about specific types of modelling.
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Probability
Probability is ordinarily used to describe an attitude of mind towards some proposition of whose truth we are not certain. The proposition of interest is usually of the form "Will a specific event occur?" The attitude of mind is of the form "How certain are we that the event will occur?" The certainty we adopt can be described in terms of a numerical measure and this number, between 0 and 1, we call probability.
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Graphical model
A graphical model is a probabilistic model for which a graph denotes the conditional independence structure between random variables. They are commonly used in probability theory, statistics¿particularly Bayesian statistics¿and machine learning.
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Foreign key
In the context of relational databases, a foreign key is a referential constraint between two tables. A foreign key is a field in a relational table that matches a candidate key of another table. The foreign key can be used to cross-reference tables. For example, say we have two tables, a CUSTOMER table that includes all customer data, and an ORDER table that includes all customer orders.
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Latent variable
In statistics, latent variables (as opposed to observable variables), are variables that are not directly observed but are rather inferred from other variables that are observed (directly measured). Mathematical models that aim to explain observed variables in terms of latent variables are called latent variable models.
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Bayesian inference
In statistics, Bayesian inference is a method of inference in which Bayes' rule is used to update the probability estimate for a hypothesis as additional evidence is learned. Bayesian updating is an important technique throughout statistics, and especially in mathematical statistics: Exhibiting a Bayesian derivation for a statistical method automatically ensures that the method works as well as any competing method, for some cases.
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Missing data
In statistics, missing data, or missing values, occur when no data value is stored for the variable in the current observation. Missing data are a common occurrence and can have a significant effect on the conclusions that can be drawn from the data.
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