Computes a sequence of marginal densities for a continuous state space Markov chain \(X_t\) where the transition probabilities can be represented as densities. The estimate of the marginal density of \(X_t\) is
This is a density in \(y\).
An instance is a representation of a look ahead estimator associated with a given stochastic kernel p and a vector of observations X.
- p : function
The stochastic kernel. A function p(x, y) that is vectorized in both x and y
- X : array_like(float)
A vector containing observations
>>> psi = LAE(p, X) >>> y = np.linspace(0, 1, 100) >>> psi(y) # Evaluate look ahead estimate at grid of points y
- p, X : see Parameters
A vectorized function that returns the value of the look ahead estimate at the values in the array y.