[Reliability Bayesian pymc-devs#474] trying to fix git index

Signed-off-by: Nathaniel <[email protected]>

Author: NathanielF

examples/case_studies/reliability_and_calibration.ipynb

@@ -8,7 +8,7 @@
     "# Reliability Statistics and Predictive Calibration\n",
     "\n",
     ":::{post} January, 2023\n",
-    ":tags: time-to-failure, prediction, calibration\n",
+    ":tags: time-to-failure, prediction, calibration, survival analysis, censored\n",
     ":category: intermediate\n",
     ":author: Nathaniel Forde\n",
     ":::"

myst_nbs/case_studies/reliability_and_calibration.myst.md

@@ -15,7 +15,7 @@ kernelspec:
 # Reliability Statistics and Predictive Calibration
 
 :::{post} January, 2023
-:tags: time-to-failure, prediction, calibration
+:tags: time-to-failure, prediction, calibration, survival analysis, censored
 :category: intermediate
 :author: Nathaniel Forde
 :::
@@ -88,7 +88,7 @@ mu, sigma = 6, 0.3
 def plot_ln_pi(mu, sigma, xy=(700, 75), title="Exact Prediction Interval for Known Lognormal"):
     failure_dist = lognorm(s=sigma, scale=np.exp(mu))
     samples = failure_dist.rvs(size=1000, random_state=100)
-    fig, axs = plt.subplots(1, 3, figsize=(20, 6))
+    fig, axs = plt.subplots(1, 3, figsize=(20, 10))
     axs = axs.flatten()
     axs[0].hist(samples, ec="black", color="slateblue", bins=30)
     axs[0].set_title(f"Failure Time Distribution: LN({mu}, {sigma})")
@@ -331,7 +331,7 @@ def plot_cdfs(actuarial_table, dist_fits=True, ax=None, title="", xy=(3000, 0.5)
         lnf = LogNormalFitter().fit(item_period["t"] + 1e-25, item_period["failed"])
         wbf = WeibullFitter().fit(item_period["t"] + 1e-25, item_period["failed"])
     if ax is None:
-        fig, ax = plt.subplots(figsize=(20, 8))
+        fig, ax = plt.subplots(figsize=(20, 10))
     ax.plot(
         actuarial_table["t"],
         actuarial_table["F_hat"],
@@ -464,7 +464,7 @@ Next we'll plot the bootstrapped data and the two estimates of coverage we achie
 ```{code-cell} ipython3
 mosaic = """AABB
             CCCC"""
-fig, axs = plt.subplot_mosaic(mosaic=mosaic, figsize=(20, 10))
+fig, axs = plt.subplot_mosaic(mosaic=mosaic, figsize=(20, 12))
 mle_rv = lognorm(s=0.53, scale=np.exp(10.128))
 axs = [axs[k] for k in axs.keys()]
 axs[0].scatter(