diff --git a/configFile.ini b/configFile.ini
index d81da6f..c2f2229 100755
--- a/configFile.ini
+++ b/configFile.ini
@@ -20,4 +20,4 @@ writeText = True
 [gracedb]
 gracedb_url = https://gracedb.ligo.org/api/
 tagnames = em_follow lvem
-gdbwrite = True
+gdbwrite = False
diff --git a/genDiskMassProbability.py b/genDiskMassProbability.py
index 050da75..5456c61 100644
--- a/genDiskMassProbability.py
+++ b/genDiskMassProbability.py
@@ -98,4 +98,4 @@ def computeEMBrightProb(self):
 	'''
 	remMass = self.computeRemMass()
 	prob	= (self.mPrimary < self.max_ns_g_mass)*(self.mSecondary < self.max_ns_g_mass) + (remMass > self.threshold)
-	return 100.0*np.sum(prob > 0.) / len(prob)
+	return prob
diff --git a/getEllipsoidSamples.py b/getEllipsoidSamples.py
index cf992a3..e19150f 100644
--- a/getEllipsoidSamples.py
+++ b/getEllipsoidSamples.py
@@ -220,6 +220,8 @@ def getSamples(graceid, mass1, mass2, chi1, network_snr, samples, PSD, fmin=30,
     NN_total = 0
     cart_grid = [[0., 0., 0.]]
     sph_grid = [[0., 0., 0.]]
+    likelihood_grid = [1.] ## Koh Ueno added
+    
     while NN < Nrandpts:
         NN_total += 1
         r = np.random.rand()
@@ -235,21 +237,35 @@ def getSamples(graceid, mass1, mass2, chi1, network_snr, samples, PSD, fmin=30,
         ### CHECK ####
         cart_grid_point = [x1, x2, x3]
         cart_grid_point = np.array(np.real( np.dot(rot, cart_grid_point)) )
-
+        
         rand_Mc = cart_grid_point[0] * lal.MSUN_SI + McSIG # Mc (kg)
         rand_eta = cart_grid_point[1] + etaSIG # eta
         rand_chi = cart_grid_point[2] + chiSIG
         ### CHECK ####
-
+        
+        ### Koh Ueno: I added the followings to introduce the correct weight for each sample.
+        ### I think we can do this computation only after "if joint_condition"
+        ### if you want to make the computational cost as small as possible.
+        ### I am wondering if we can use the modified "gam"
+        ###  (i.e., the gam which gam_prior is added to)
+        ### for the likelihood computation.
+        diff_rand_sample = [(rand_Mc - McSIG) / lal.MSUN_SI, rand_eta - etaSIG, rand_chi - chiSIG]
+        likelihood_tmp = np.dot( np.dot(gam, diff_rand_sample), diff_rand_sample)
+        likelihood = np.exp( -0.5 * network_snr**2 * likelihood_tmp.item(0) )
+        ### Koh Ueno: ends
+        
         condition1 = rand_eta > 0
         condition2 = rand_eta <= 0.25
         condition3 = np.abs(rand_chi) < 1.0
         joint_condition = condition1 * condition2 * condition3
         if joint_condition:
             cart_grid.append( [cart_grid_point[0], cart_grid_point[1], cart_grid_point[2]] ) ## CHECK
+            likelihood_grid.append(likelihood) ### Koh Ueno added
             NN += 1
     cart_grid = np.array(cart_grid)
     sph_grid = np.array(sph_grid)
+    likelihood_grid = np.array(likelihood_grid)
+    
     if logFile:
         log.writelines(str(datetime.datetime.today()) + '\t' + 'Selected ' + str(NN) + ' points from ' + str(NN_total) + ' random samples within the ellipsoid \n')
     else:
@@ -266,7 +282,7 @@ def getSamples(graceid, mass1, mass2, chi1, network_snr, samples, PSD, fmin=30,
     rand_Mcs = rand_dMcs_MSUN * lal.MSUN_SI + McSIG # Mc (kg)
     rand_etas = rand_detas + etaSIG # eta
     rand_chis = rand_dChis + chiSIG
-
+    
     # Prune points with unphysical values of eta from cart_grid
     rand_etas = np.array(map(partial(lsu.sanitize_eta, exception=np.NAN), rand_etas))
     cart_grid = np.transpose((rand_Mcs,rand_etas,rand_chis)) #### CHECK ####
@@ -330,7 +346,7 @@ def getSamples(graceid, mass1, mass2, chi1, network_snr, samples, PSD, fmin=30,
         if show:
             pl.show()
 
-    return outgrid
+    return outgrid, likelihood_grid # KU
 
 
 
diff --git a/sourceClassDriver.py b/sourceClassDriver.py
index 07998aa..7099923 100755
--- a/sourceClassDriver.py
+++ b/sourceClassDriver.py
@@ -146,17 +146,17 @@ def readCoinc(CoincFile):
         File.writelines(graceid + '\t' + str(mass1) + '\t' +  str(mass2) + '\t' + str(chi1) + '\t' + str(snr) + '\n')
         File.close()
 
-    samples_sngl = getSamples(graceid, mass1, mass2, chi1, snr, ellipsoidSample, {ifo + '=' + psd_path + '/psd_' + graceid + '.xml.gz'}, fmin=f_low, NMcs=10, NEtas=10, NChis=10, mass1_cut=mass1_cut, chi1_cut=chi1_cut, lowMass_approx=lowMass_approx, highMass_approx=highMass_approx, Forced=forced, logFile=logFileName, saveData=True)
+    samples_sngl, likelihood = getSamples(graceid, mass1, mass2, chi1, snr, ellipsoidSample, {ifo + '=' + psd_path + '/psd_' + graceid + '.xml.gz'}, fmin=f_low, NMcs=10, NEtas=10, NChis=10, mass1_cut=mass1_cut, chi1_cut=chi1_cut, lowMass_approx=lowMass_approx, highMass_approx=highMass_approx, Forced=forced, logFile=logFileName, saveData=True)
     log.writelines(str(datetime.datetime.today()) + '\t' + 'Created ambiguity ellipsoid samples\n')
 
     ### Currently NaNs are generated when the ellipsoid generation failed. This will be changed in subsequent version. ###
     if ~np.any( np.isnan(samples_sngl[0]) ):
         diskMassObject_sngl = genDiskMassProbability.genDiskMass(samples_sngl, 'test', remMassThreshold)
         [NS_prob_1_sngl, NS_prob_2_sngl, diskMass_sngl] = diskMassObject_sngl.fromEllipsoidSample()
-        #em_bright_prob_sngl = np.sum((diskMass_sngl > 0.)*100./len(diskMass_sngl))
-        em_bright_prob_sngl = diskMassObject_sngl.computeEMBrightProb() # RE: Probability using new EM bright boundary
-	#NS_prob_1_sngl, NS_prob_2_sngl, em_bright_prob_sngl = diskMassObject_sngl.computeEMBrightProb()
-
+        prob = diskMassObject_sngl.computeEMBrightProb() # RE: Probability using new EM bright boundary
+        likelihood_br = likelihood[np.where(prob > 0.)] # KU
+        em_bright_prob_sngl_old = np.sum(prob > 0.) / len(prob)
+        em_bright_prob_sngl_new = np.sum(likelihood_br) / np.sum(likelihood)
     else:
         log.writelines(str(datetime.datetime.today()) + '\t' + 'Return was NaNs\n')
         [NS_prob_2_sngl, em_bright_prob_sngl] = [0., 0.]