Sentence examples for model f x from inspiring English sources

Note that for this model, f(x)=E(Y|X=x) and in fact the conditional distribution Pr(Y|X) depends on X only through the conditional mean f(x).

The multilevel scaling model used consists of two components a conditional item response model, f x (x; ξ ξ|θ), and a population model, f θ (θ; γ, Σ, W).

The conditional item response model and the population model are combined to obtain the unconditional, or marginal, item response model: f x x ; γ, Σ, W = ∫ θ f x x ; ξ θ f θ θ ; γ, Σ, W d θ (1).

We consider a linear model f(x) = ⟨x,β⟩.

The starting point is a straight line model f(X) =  β1 X.

We can see this by using the general model f(x) =  n ·  x m.

M j were often included in the models; f(x) is a function modeling the auto-correlation structure within subjects.

In many bio-system models, f (x, θ) is autonomous system and linear in θ as follows: (8) x ˙ (t ) = Φ (x (t ) ) θ,     x (t 0 ) = x 0. where Φ x) ϵ R n × k is a matrix and its elements are a function of the state x.

Boosting methods construct such a function F X) by additive combinations of M 'base' learners h(X) (e.g. linear regression models) F (X ) = ∑ m = 1 M β m h (X ; θ m ), with corresponding combination coefficients β m and parameters θ m of m-th learner (Friedman, 2001; Bühlmann and Hothorn, 2007).

Consider the pre-Hilbert space of models f (x; a) = Σ a x ·K(x', x) where the sums are initially over finitely many x ;where K (u, v), is a piecewise continuous, bounded symmetric, non-negative kernel function on V × V, positive at diagonal points (u u); and where the matrix K (z i, z j) is positive semi-definite for any finite { z i} ⊆ V (positive definite for distinct z i ).

The model f(p(x i ) = l + b log (x i /t)) if x i > t     (2) has been previously proposed together with a likelihood maximisation method for estimate the threshold and its confidence interval [ 1].