Sentence examples for marginal distribution at from inspiring English sources

However the marginal distribution at follow up time-points is not as clearly shown, therefore we recommend inclusion of a table above the stacked bars showing the marginal distribution at each time-point.

Therefore, we just have to compute the marginal distribution at the end of the sequence and sum up the contribution of each state.

It is well known that the magnitude μ of the second eigenvalue of the transition matrix plays here a key role since the absolute difference between the marginal distribution at position i and the stationary distribution is O(μ i ).

Posterior distributions f i (x i,k |y 1 : k ), i = 1 ⋯ N k in Equation 13 are marginal distributions at time step k, and existence probabilities p i,k, i = 1 ⋯ N k in Equation 14 are existence probabilities of the marginal distributions at time step k.

A5: The new marginal distributions at time step k are Gaussian distribution and are generated from observations at time step k as: {f}_{gamma}^jleft({mathbf{x}}_{i,k}right)=Nleft({mathbf{x}}_{i,k};{mathbf{m}}_{gamma}^j,{mathbf{P}}_{gamma}^jright),j=1cdots M (17).

A4: Each target follows a linear Gaussian dynamic model, and the sensor has a linear Gaussian measurement model, i.e., A5: The new marginal distributions at time step k are Gaussian distribution and are generated from observations at time step k as: where ( {mathbf{P}}_{gamma}^j ) is the covariance matrix of new distribution j, and ( {mathbf{m}}_{gamma}^j ) is the mean of new distribution j.

Knots were placed at fixed quantiles of the predictor's marginal distribution as suggested by Harrell (2001).

The primary concern of the theory of stochastic processes is not this marginal distribution of N t) at a particular time but rather the evolution of N t) over time.

This class is used by the class fitness_inference to calculate the marginal distribution of fitness at each external and internal node of a given tree.

The age at event for each subject in each source population was generated from a standard Cox model with time-dependent covariates, using a permutation algorithm described elsewhere and assuming Weibull marginal distribution of age at event [ 4, 22, 23].

Each node t computes its belief, the posterior marginal distribution of 2D position at iteration i, by taking a product of its local potential with the messages from its set of neighbors (9).