lae¶
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
\[\frac{1}{n} \sum_{i=0}^n p(X_{t-1}^i, y)\]
This is a density in \(y\).
References¶
https://lectures.quantecon.org/py/stationary_densities.html
-
class
quantecon.lae.
LAE
(p, X)[source]¶ Bases:
object
An instance is a representation of a look ahead estimator associated with a given stochastic kernel p and a vector of observations X.
Parameters: - 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
Examples
>>> psi = LAE(p, X) >>> y = np.linspace(0, 1, 100) >>> psi(y) # Evaluate look ahead estimate at grid of points y
Attributes: - p, X : see Parameters
Methods
__call__
(y)A vectorized function that returns the value of the look ahead estimate at the values in the array y.