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Approximating a function consists of replacing it by another function of simpler form that may be used as its surrogate. This strategy is used frequently in numerical integration where, instead of computing ∫ (), one carries out the exact computation of ∫ (), being a function simple to integrate (e.g. a polynomial), as we will see in the next chapter. In other instances the function may be available only partially through its values at some selected points. In these cases we aim at constructing a continuous function that could represent the empirical law which is behind the finite set of data. We provide some examples which illustrate this kind of approach.

Approximating a function consists of replacing it by another function of simpler form that may be used as its surrogate. This strategy is used frequently in numerical integration where, instead of computing ∫ (), one carries out the exact computation of ∫ (), being a function simple to integrate (e.g. a polynomial), as we will see in the next chapter. In other instances the function may be available only partially through its values at some selected points. In these cases we aim at constructing a continuous function that could represent the empirical law which is behind the finite set of data. We provide some examples which illustrate this kind of approach.

Approximating a function consists of replacing it by another function of simpler form that may be used as its surrogate. This strategy is used frequently in numerical integration where, instead of computing ∫ (), one carries out the exact computation of ∫ (), being a function simple to integrate (e.g. a polynomial), as we will see in the next chapter. In other instances the function may be available only partially through its values at some selected points. In these cases we aim at constructing a continuous function that could represent the empirical law which is behind the finite set of data. We provide some examples which illustrate this kind of approach.

Approximating a function consists of replacing it by another function of simpler form that may be used as its surrogate. This strategy is used frequently in numerical integration where, instead of computing ∫ (), one carries out the exact computation of ∫ (), being a function simple to integrate (e.g. a polynomial), as we will see in the next chapter. In other instances the function may be available only partially through its values at some selected points. In these cases we aim at constructing a continuous function that could represent the empirical law which is behind the finite set of data. We provide some examples which illustrate this kind of approach.

Approximating a function consists of replacing it by another function of simpler form that may be used as its surrogate. This strategy is used frequently in numerical integration where, instead of computing ∫ (), one carries out the exact computation of ∫ (), being a function simple to integrate (e.g. a polynomial), as we will see in the next chapter. In other instances the function may be available only partially through its values at some selected points. In these cases we aim at constructing a continuous function that could represent the empirical law which is behind the finite set of data. We provide some examples which illustrate this kind of approach.

Approximating a function consists of replacing it by another function of simpler form that may be used as its surrogate. This strategy is used frequently in numerical integration where, instead of computing ∫ (), one carries out the exact computation of ∫ (), being a function simple to integrate (e.g. a polynomial), as we will see in the next chapter. In other instances the function may be available only partially through its values at some selected points. In these cases we aim at constructing a continuous function that could represent the empirical law which is behind the finite set of data. We provide some examples which illustrate this kind of approach.

Approximating a function consists of replacing it by another function of simpler form that may be used as its surrogate. This strategy is used frequently in numerical integration where, instead of computing ∫ (), one carries out the exact computation of ∫ (), being a function simple to integrate (e.g. a polynomial), as we will see in the next chapter. In other instances the function may be available only partially through its values at some selected points. In these cases we aim at constructing a continuous function that could represent the empirical law which is behind the finite set of data. We provide some examples which illustrate this kind of approach.

Approximating a function consists of replacing it by another function of simpler form that may be used as its surrogate. This strategy is used frequently in numerical integration where, instead of computing ∫ (), one carries out the exact computation of ∫ (), being a function simple to integrate (e.g. a polynomial), as we will see in the next chapter. In other instances the function may be available only partially through its values at some selected points. In these cases we aim at constructing a continuous function that could represent the empirical law which is behind the finite set of data. We provide some examples which illustrate this kind of approach.

Approximating a function consists of replacing it by another function of simpler form that may be used as its surrogate. This strategy is used frequently in numerical integration where, instead of computing ∫ (), one carries out the exact computation of ∫ (), being a function simple to integrate (e.g. a polynomial), as we will see in the next chapter. In other instances the function may be available only partially through its values at some selected points. In these cases we aim at constructing a continuous function that could represent the empirical law which is behind the finite set of data. We provide some examples which illustrate this kind of approach.