AdditionalDistributions

config/continuous/gamma.py

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+import streamlit as st
+import torch
+
+GAMMA = {
+    "params": lambda: {
+        "alpha": st.sidebar.slider("Alpha (α)", 0.1, 10.0, 2.0, 0.1),
+        "beta": st.sidebar.slider("Beta (β)", 0.1, 10.0, 2.0, 0.1),
+    },
+    "dist": lambda p: torch.distributions.Gamma(p["alpha"], p["beta"]),
+    "support": lambda p: (0, None),
+}

config/continuous/laplace.py

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+import streamlit as st
+import torch
+
+LAPLACE = {
+    "params": lambda: {
+        "mean": st.sidebar.slider("Mean (μ)",  -10.0, 10.0, 0.0),
+        "scale": st.sidebar.slider("Scale (λ)", 0.1, 10.0, 1.0),
+    },
+    "dist": lambda p: torch.distributions.Laplace(p["mean"], p["scale"]),
+    "support": None,
+}

config/discrete/categorical.py

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+import streamlit as st
+import torch
+
+CATEGORICAL = {
+    "params": lambda: {
+        "probs": st.sidebar.text_input("Probabilities (comma-separated)", "0.1,0.2,0.3,0.4"),
+    },
+    "dist": lambda p: torch.distributions.Categorical(torch.tensor([float(x) for x in p["probs"].split(',')])),
+    "support": lambda p: (0, len(p["probs"].split(',')) - 1),
+}