Sentence examples for model p x from inspiring English sources

The motion model p(X k |X k - 1) predicts the state X k given the previous state X k- 1.

where a dynamic model p(x t,τ |x t,τ-1) defines the possible positions of the unknown node x t,τ in current time instant τ, given the estimated position from the previous time instant.

The process of finding an appropriate probabilistic classification model p(x i,y i ) involves relating x i probabilistically to its associated y i, where p(x i,y i ) refers to the probability of (x i, y i ) occurring.

The motion model p(X k |X k - 1) is conditioned directly on the immediate preceding state and independent of the earlier history if the motion dynamics are assumed to form a temporal Markov chain.

Suppose there is a response Y∈{0,1} related to k predictors (X 1,…,X k ) via the logistic regression model p(x ′ β)=P(Y=1 | X 1=x 1,…,X k =x k ) where p(mathbf{x}^{prime}mathbf{beta})=expleft{ mathbf{x}^{prime }mathbf{beta}right} /left(1+expleft{ mathbf{x}^{prime }mathbf{beta}right} right).

Given a motion model p ( x t | x t − 1, x ̇ t ) and a sensing model p z t |x t ), we recursively apply a prediction phase where we incorporate motion to update the state and an update phase where the sensing data is used to compute the state's posterior distribution.

The flexibility of this model is provided by the choices for the response sub-model p (Y i | Z i, Θ Z i, Θ 0 ) and the profile sub-model p (X i | Z i, Θ Z i, Θ 0 ).

For the purpose of illustration, we will use the special case of a unidimensional model with dichotomous items (Rasch model): logit P X ni = 1 = θ n − b i, (1).

where α is a normalization constant, P et + 1|Xt + 1) is obtained from the sensor model, P Xt + 1|x t ) is the transition model and P(x t |e1 t) is the current state distribution.

The wavelet coefficients from the reference image subbands are fitted using the generalized Gaussian density model: p GGD ( x ; α, β ) = β 2 α Γ ( 1 / β ) e ( x / α ) β (1).

According to the price model p h (X h ), the model can guarantee the growth of the energy price with the increase of X h, which will effectively convince the consumers to shift their peak-time consumption to off-peak hours.