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In statistics education, informal inferential reasoning (also called informal inference) refers to the process of making a generalization based on data (samples) about a wider universe (population/process) while taking into account uncertainty without using the formal statistical procedure or methods (e.g. P-values, t-test, hypothesis testing, significance test).
Additionally, the term 'inference' has also been applied to the process of generating predictions from trained neural networks. In this context, an 'inference engine' refers to the system or hardware performing these operations. This type of inference is widely used in applications ranging from image recognition to natural language processing.
Rules of inference are syntactical transform rules which one can use to infer a conclusion from a premise to create an argument. A set of rules can be used to infer any valid conclusion if it is complete, while never inferring an invalid conclusion, if it is sound.
Inference using vague concepts. Inferences that involve reasoning near the boundaries of a vague concept are often uncertain. 8. Finding expected utility. This is the problem of choosing between actions whose consequences are uncertain. In such a case, a choice may be made based on the likelihoods of the various outcomes with their desirability. 9.
Deductive reasoning is the psychological process of drawing deductive inferences.An inference is a set of premises together with a conclusion. This psychological process starts from the premises and reasons to a conclusion based on and supported by these premises.
A Mastermind player uses abduction to infer the secret colors (top) from summaries (bottom left) of discrepancies in their guesses (bottom right).. Abductive reasoning (also called abduction, [1] abductive inference, [1] or retroduction [2]) is a form of logical inference that seeks the simplest and most likely conclusion from a set of observations.
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