diff --git a/TrainingExtensions/torch/test/python/experimental/v2/test_encoding_analyzer.py b/TrainingExtensions/torch/test/python/experimental/v2/test_encoding_analyzer.py
index 7d99ebbad73..9b54842dd14 100644
--- a/TrainingExtensions/torch/test/python/experimental/v2/test_encoding_analyzer.py
+++ b/TrainingExtensions/torch/test/python/experimental/v2/test_encoding_analyzer.py
@@ -36,7 +36,7 @@
 # =============================================================================
 import torch
 import pytest
-from aimet_torch.experimental.v2.quantization.encoding_analyzer import get_encoding_analyzer_cls, CalibrationMethod
+from aimet_torch.experimental.v2.quantization.encoding_analyzer import get_encoding_analyzer_cls, CalibrationMethod, MseEncodingAnalyzer, PercentileEncodingAnalyzer, SqnrEncodingAnalyzer
 
 
 class TestEncodingAnalyzer():
@@ -162,3 +162,76 @@ def test_update_stats_incompatible_dimension(self):
         encoding_analyzer_1 = get_encoding_analyzer_cls(CalibrationMethod.MinMax, [3, 4])
         with pytest.raises(RuntimeError):
             encoding_analyzer_1.update_stats(torch.randn(2, 3, 5))
+
+@pytest.mark.skip('Tests skipped due to TDD')
+class TestHistogramEncodingAnalyzer:
+    @pytest.fixture
+    def histogram_based_encoding_analyzers(self, request):
+        min_max_shape = request.param[0]
+        num_bins = request.param[1]
+
+        mse_encoding_analyzer = MseEncodingAnalyzer(shape= min_max_shape, num_bins = num_bins)
+        percentile_encoding_analyzer = PercentileEncodingAnalyzer(percentile=99, shape= min_max_shape, num_bins = num_bins)
+        sqnr_encoding_analyzer = SqnrEncodingAnalyzer(shape= min_max_shape, num_bins = num_bins)
+        encoding_analyzer_list = [mse_encoding_analyzer, percentile_encoding_analyzer, sqnr_encoding_analyzer]
+        yield encoding_analyzer_list
+
+    @pytest.mark.parametrize('num_bins', [-1, 0])
+    def test_invalid_bin_value(self, num_bins):
+        min_max_shape = (1,)
+        with pytest.raises(ValueError):
+            MseEncodingAnalyzer(num_bins=num_bins)
+        
+        with pytest.raises(ValueError):
+            PercentileEncodingAnalyzer(num_bins = num_bins, percentile=99, shape=min_max_shape)
+        
+        with pytest.raises(ValueError):
+            SqnrEncodingAnalyzer(num_bins = num_bins, shape=min_max_shape)
+        
+    @pytest.mark.parametrize("histogram_based_encoding_analyzers", [((1,), 3)], indirect=True)
+    def test_merge_stats(self, histogram_based_encoding_analyzers):
+        for encoding_analyzer in histogram_based_encoding_analyzers:
+            input_tensor_1 = [2.0, 3.5, 4.2, 5.0]
+            encoding_analyzer.update_stats(input_tensor_1)
+            assert encoding_analyzer.stats.min == 2
+            assert encoding_analyzer.stats.max == 5
+            assert encoding_analyzer.stats.histogram == [1, 1, 2]
+            assert encoding_analyzer.stats.bin_edges == [2, 3, 4, 5]
+            
+            input_tensor_2 = [5.3, 6.4, 7.0, 8.0]
+            encoding_analyzer.update_stats(input_tensor_2)
+            assert encoding_analyzer.stats.min == 2
+            assert encoding_analyzer.stats.max == 8
+            assert encoding_analyzer.stats.histogram == [2, 3, 3]
+            assert encoding_analyzer.stats.bin_edges == [2, 4, 6, 8]
+    
+    @pytest.mark.parametrize("histogram_based_encoding_analyzers", [((1,), 3)], indirect=True)
+    def test_merge_stats_same_tensor(self, histogram_based_encoding_analyzers):
+        for encoding_analyzer in histogram_based_encoding_analyzers:
+            input_tensor_1 = [2.0, 3.5, 4.2, 5.0]
+            encoding_analyzer.update_stats(input_tensor_1)
+            assert encoding_analyzer.stats.min == 2
+            assert encoding_analyzer.stats.max == 5
+            assert encoding_analyzer.stats.histogram == [1, 1, 2]
+            assert encoding_analyzer.stats.bin_edges == [2, 3, 4, 5]
+            
+            input_tensor_2 = [2.0, 3.5, 4.2, 5.0]
+            encoding_analyzer.update_stats(input_tensor_2)
+            assert encoding_analyzer.stats.min == 2
+            assert encoding_analyzer.stats.max == 5
+            assert encoding_analyzer.stats.histogram == [2, 2, 4]
+            assert encoding_analyzer.stats.bin_edges == [2, 3, 4, 5]
+    
+    @pytest.mark.parametrize("histogram_based_encoding_analyzers", [((1,), 5)], indirect=True)
+    def test_handle_outliers(self, histogram_based_encoding_analyzers):
+        for encoding_analyzer in histogram_based_encoding_analyzers:
+            input_tensor =  torch.arange(start=0, end=100, step=0.5, dtype=torch.float)
+            data_type = encoding_analyzer.observer.stats.min.dtype
+            outliers = torch.tensor([torch.finfo(data_type).tiny, torch.finfo(data_type).max])
+            input_tensor = torch.cat((input_tensor, outliers), 1)
+            encoding_analyzer.update_stats(input_tensor)
+            assert encoding_analyzer.stats.min == 0
+            assert encoding_analyzer.stats.max == 99.5
+            assert encoding_analyzer.stats.histogram == [40, 40, 40, 40, 40]
+            assert encoding_analyzer.stats.bin_edge == [0, 19.9, 39.8, 59.7, 79.6, 99.5]
+