Revert "add RV for AR"

This reverts commit acc4fe9.

Author: mjhajharia

pymc/distributions/timeseries.py

@@ -22,8 +22,6 @@
 from pymc.distributions.continuous import Flat, Normal, get_tau_sigma
 from pymc.distributions.shape_utils import to_tuple
 
-from aesara.tensor.random.op import RandomVariable
-
 __all__ = [
     "AR1",
     "AR",
@@ -34,39 +32,49 @@
     "MvStudentTRandomWalk",
 ]
 
-class ARRV(RandomVariable):
-    name = "AR"
-    ndim_supp = 0
-    ndims_params = [1, 0, 0]
-    dtype = "floatX"
-    _print_name = ("AR", "\\operatorname{AR}")
-    
-    #set default values for parameters
-    def __call__(self, phi, mu=0.0, sigma=1.0, init=None **kwargs) -> TensorVariable:
-        return super().__call__(phi, mu, sigma,init **kwargs)
-
-    @classmethod
-    def rng_fn(
-            cls,
-            rng: np.random.default_rng(),
-            phi: np.ndarray,
-            mu: np.ndarray,
-            sigma: np.ndarray,
-            size: Tuple[int, ...],
-            init: float) -> np.ndarray:
-            
-        # size parameter *should* be necessary for time series generation
-        if size is None:
-            raise ValueError('Specify size')
-                
-        # sign conventions used in signal.lfilter (or signal processing)
-        phi = np.r_[1, -phi] # add zero-lag and negate
-            
-        #ifilter convolutes x with the coefficient theta to create a linear recurrence relation and generate the AR process
-        if init is None:
-            init = rng.normal(loc=mu, scale=sigma,size=size)
-        return signal.lfilter(phi, init, axis=-1)
 
+class AR1(distribution.Continuous):
+    """
+    Autoregressive process with 1 lag.
+
+    Parameters
+    ----------
+    k: tensor
+       effect of lagged value on current value
+    tau_e: tensor
+       precision for innovations
+    """
+
+    def __init__(self, k, tau_e, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.k = k = at.as_tensor_variable(k)
+        self.tau_e = tau_e = at.as_tensor_variable(tau_e)
+        self.tau = tau_e * (1 - k ** 2)
+        self.mode = at.as_tensor_variable(0.0)
+
+    def logp(self, x):
+        """
+        Calculate log-probability of AR1 distribution at specified value.
+
+        Parameters
+        ----------
+        x: numeric
+            Value for which log-probability is calculated.
+
+        Returns
+        -------
+        TensorVariable
+        """
+        k = self.k
+        tau_e = self.tau_e  # innovation precision
+        tau = tau_e * (1 - k ** 2)  # ar1 precision
+
+        x_im1 = x[:-1]
+        x_i = x[1:]
+        boundary = Normal.dist(0.0, tau=tau).logp
+
+        innov_like = Normal.dist(k * x_im1, tau=tau_e).logp(x_i)
+        return boundary(x[0]) + at.sum(innov_like)
 
 
 class AR(distribution.Continuous):