diff --git a/GPy/likelihoods/ordinal.py b/GPy/likelihoods/ordinal.py
deleted file mode 100644
index b6855e54..00000000
--- a/GPy/likelihoods/ordinal.py
+++ /dev/null
@@ -1,47 +0,0 @@
-# Copyright (c) 2014 The GPy authors (see AUTHORS.txt)
-# Licensed under the BSD 3-clause license (see LICENSE.txt)
-
-
-import sympy as sym
-from GPy.util.symbolic import gammaln, normcdfln, normcdf, IndMatrix, create_matrix
-import numpy as np
-import link_functions
-from symbolic import Symbolic
-from scipy import stats
-
-class Ordinal(Symbolic):
-    """
-    Ordinal
-
-    .. math::
-        p(y_{i}|\pi(f_{i})) = \left(\frac{r}{r+f_i}\right)^r \frac{\Gamma(r+y_i)}{y!\Gamma(r)}\left(\frac{f_i}{r+f_i}\right)^{y_i}
-
-    .. Note::
-        Y takes non zero integer values..
-        link function should have a positive domain, e.g. log (default).
-
-    .. See also::
-        symbolic.py, for the parent class
-    """
-    def __init__(self, categories=3, gp_link=None):
-        if gp_link is None:
-            gp_link = link_functions.Identity()
-
-        dispersion = sym.Symbol('width', positive=True, real=True)
-        y_0 = sym.Symbol('y_0', nonnegative=True, integer=True)
-        f_0 = sym.Symbol('f_0', positive=True, real=True) 
-        log_pdf = create_matrix('log_pdf', 1, categories)
-        log_pdf[0] = normcdfln(-f_0)
-        if categories>2:
-            w = create_matrix('w', 1, categories)
-            log_pdf[categories-1] = normcdfln(w.sum() + f_0)
-            for i in range(1, categories-1):
-                log_pdf[i] = sym.log(normcdf(w[0, 0:i-1].sum() + f_0) - normcdf(w[0, 0:i].sum()-f_0) )
-        else:
-            log_pdf[1] = normcdfln(f_0)
-        log_pdf.index_var = y_0
-        super(Ordinal, self).__init__(log_pdf=log_pdf, gp_link=gp_link, name='Ordinal')
-
-        # TODO: Check this.
-        self.log_concave = True
-