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| author | Shivesh Mandalia <shivesh.mandalia@outlook.com> | 2020-03-03 02:49:07 +0000 |
|---|---|---|
| committer | Shivesh Mandalia <shivesh.mandalia@outlook.com> | 2020-03-03 02:49:07 +0000 |
| commit | c06f513f9c3461925eee77bda0ce5bdcbb7cfb2c (patch) | |
| tree | 9ec9c228180eeab5b7cc6b4a34a147136d47efcb /examples/inference.ipynb | |
| parent | 8e0290d20a97a34bb0227755c2ee8f6ed0dcce22 (diff) | |
| download | GolemFlavor-c06f513f9c3461925eee77bda0ce5bdcbb7cfb2c.tar.gz GolemFlavor-c06f513f9c3461925eee77bda0ce5bdcbb7cfb2c.zip | |
slightly reluctantly use american spelling for consistency
Diffstat (limited to 'examples/inference.ipynb')
| -rw-r--r-- | examples/inference.ipynb | 6 |
1 files changed, 3 insertions, 3 deletions
diff --git a/examples/inference.ipynb b/examples/inference.ipynb index 7457f66..129fd86 100644 --- a/examples/inference.ipynb +++ b/examples/inference.ipynb @@ -11,7 +11,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In this example, we will take the fake data generated in the `tutorial.ipynb` example and use it to make an inference the source flavour composition using Bayesian techniques." + "In this example, we will take the fake data generated in the `tutorial.ipynb` example and use it to make an inference the source flavor composition using Bayesian techniques." ] }, { @@ -324,7 +324,7 @@ " source_angles = llh_paramset.from_tag(ParamTag.SRCANGLES, values=True)\n", " source_composition = angles_to_fr(source_angles)\n", "\n", - " # Calculate the expected measured flavour composition for our sampled values\n", + " # Calculate the expected measured flavor composition for our sampled values\n", " measured_composition = u_to_fr(source_composition, sm_u)\n", "\n", " # Convert flavor angles to flavor compositions for the injected parameters\n", @@ -588,7 +588,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Great! Looks like our inference of the source flavour composition reflects the injected value $(1:0:0)_S$. Here, the credbility regions include the effect of smearing as well as our uncertainity about the values of the mixing matrix, which is why the values are not exactly at the injected $(1:0:0)_S$ value.\n", + "Great! Looks like our inference of the source flavor composition reflects the injected value $(1:0:0)_S$. Here, the credbility regions include the effect of smearing as well as our uncertainity about the values of the mixing matrix, which is why the values are not exactly at the injected $(1:0:0)_S$ value.\n", "\n", "In a real analysis, an ensemble of nuisance parameters is usually required, related to uncertainties arising from things such as the astrophysical flux, detector calibration and backgrounds from atmospherically produced neutrinos. All these effects come into play when making inferences and careful analysis must be done for each in order to minimize potential biases." ] |