diff --git a/GPy/inference/optimization/stochastics.py b/GPy/inference/optimization/stochastics.py
new file mode 100644
index 00000000..abde20d3
--- /dev/null
+++ b/GPy/inference/optimization/stochastics.py
@@ -0,0 +1,48 @@
+'''
+Created on 9 Oct 2014
+
+@author: maxz
+'''
+
+class StochasticStorage(object):
+    '''
+    This is a container for holding the stochastic parameters,
+    such as subset indices or step length and so on.
+    '''
+    def __init__(self, model):
+        """
+        Initialize this stochastic container using the given model
+        """
+
+    def do_stochastics(self):
+        """
+        Update the internal state to the next batch of the stochastic
+        descent algorithm.
+        """
+        pass
+
+class SparseGPMissing(StochasticStorage):
+    def __init__(self, model, batchsize=1):
+        self.d = xrange(model.Y_normalized.shape[1])
+
+class SparseGPStochastics(StochasticStorage):
+    """
+    For the sparse gp we need to store the dimension we are in,
+    and the indices corresponding to those
+    """
+    def __init__(self, model, batchsize=1):
+        import itertools
+        self.batchsize = batchsize
+        if self.batchsize == 1:
+            self.dimensions = itertools.cycle(range(model.Y_normalized.shape[1]))
+        else:
+            import numpy as np
+            self.dimensions = lambda: np.random.choice(model.Y_normalized.shape[1], size=batchsize, replace=False)
+        self.d = None
+        self.do_stochastics()
+
+    def do_stochastics(self):
+        if self.batchsize == 1:
+            self.d = [self.dimensions.next()]
+        else:
+            self.d = self.dimensions()
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