tighter

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

Author: NathanielF

examples/case_studies/bayesian_sem_workflow.ipynb

@@ -7044,9 +7044,9 @@
    "source": [
     "## Conclusion: Workflow and Craft in Statistical Modelling\n",
     "\n",
-    "We have now seen how to articulate Structural Equation models and their variants in PyMC. The SEM workflow is, at heart, Bayesian in temperament. Hypothesise and construct. Construct then Estimate. Estimate and check. Check then refine. Refine then expand... Both disciplines reject the checklist mentality of “fit once, report, move on.” Instead, they cultivate a focused, deliberate practice. Each demands an apprenticeship in which skill is honed: skill to see how assumptions shape understanding, and how the world resists the imposition of false structures. Skill to find the right structures. Each iteration is a dialogue between theory and evidence. At each juncture we ask whether this model speaks true? Whether this structure reflects the facts to hand. \n",
+    "We have now seen how to articulate Structural Equation models and their variants in PyMC. The SEM workflow is, at heart, Bayesian in temperament. Hypothesise and construct. Construct then Estimate. Estimate and check. Check then refine. Refine then expand... Both disciplines reject the checklist mentality of “fit once, report, move on.” Instead, they cultivate a focused, deliberate practice. Each demands an apprenticeship in which skill is honed: skill to see how assumptions shape understanding, and how the world resists the imposition of false structures. Skill to find the right structures. Each iteration is a dialogue between theory and evidence. At each juncture we ask whether this model speaks true? Whether this structure reflects the facts to hand? \n",
     "\n",
-    "Of course, the craft ideal has its limits. The Bayesian workflow, for all its discipline and transparency, cannot by itself guarantee truth. Every model remains a conditional story, framed by the priors we choose and the theories we inherit. Iteration can refine these frames, but it can also entrench them. The aspiration to know what our models say must also include the humility to accept what they cannot reveal.\n",
+    "Of course, the craft ideal has its limits. The Bayesian workflow, for all its discipline and transparency, cannot by itself guarantee truth. Every model remains a conditional story, framed by the priors we choose and the theories we inherit. Iteration can refine these frames, but it can also entrench them. The aspiration to know what our models say must also include the humility to accept what they cannot reveal. Theory driven models, developed through iterative checking and clear communication, are more robust to misspecification, more transparent to collaborators, and more resistant to the subtle distortions of overfitting and overconfidence.\n",
     "\n",
     "In the end, the value of craft in statistical modeling lies not in merely improving benchmark metrics, but in the depth of understanding we cultivate through careful communication and justification. The Bayesian workflow reminds us that modeling is not the automation of insight, but its deliberate construction. Our workflow is a process of listening, revising, and re-articulating until the model speaks clearly. Like any craft, its worth is measured not by throughput but by fidelity: how honestly our structure reflects the world it seeks to describe. Each diagnostic, each posterior check, each refinement of a latent path is a form of attention — a small act of resistance against the flattening logic of target metrics and checklists. These constructive habits and reflective practices are the source of fulfillment in the work. __To practice modeling as craft is to reclaim pride in knowing what our models say, what they do not say, and what they imply__  -  and to find, in that discipline and skilled attention, the satisfaction of meaningful work and useful science.\n"
    ]

examples/case_studies/bayesian_sem_workflow.myst.md

@@ -1558,9 +1558,9 @@ The same workflow extends seamlessly to computational agents, where latent varia
 
 ## Conclusion: Workflow and Craft in Statistical Modelling
 
-We have now seen how to articulate Structural Equation models and their variants in PyMC. The SEM workflow is, at heart, Bayesian in temperament. Hypothesise and construct. Construct then Estimate. Estimate and check. Check then refine. Refine then expand... Both disciplines reject the checklist mentality of “fit once, report, move on.” Instead, they cultivate a focused, deliberate practice. Each demands an apprenticeship in which skill is honed: skill to see how assumptions shape understanding, and how the world resists the imposition of false structures. Skill to find the right structures. Each iteration is a dialogue between theory and evidence. At each juncture we ask whether this model speaks true? Whether this structure reflects the facts to hand. 
+We have now seen how to articulate Structural Equation models and their variants in PyMC. The SEM workflow is, at heart, Bayesian in temperament. Hypothesise and construct. Construct then Estimate. Estimate and check. Check then refine. Refine then expand... Both disciplines reject the checklist mentality of “fit once, report, move on.” Instead, they cultivate a focused, deliberate practice. Each demands an apprenticeship in which skill is honed: skill to see how assumptions shape understanding, and how the world resists the imposition of false structures. Skill to find the right structures. Each iteration is a dialogue between theory and evidence. At each juncture we ask whether this model speaks true? Whether this structure reflects the facts to hand? 
 
-Of course, the craft ideal has its limits. The Bayesian workflow, for all its discipline and transparency, cannot by itself guarantee truth. Every model remains a conditional story, framed by the priors we choose and the theories we inherit. Iteration can refine these frames, but it can also entrench them. The aspiration to know what our models say must also include the humility to accept what they cannot reveal.
+Of course, the craft ideal has its limits. The Bayesian workflow, for all its discipline and transparency, cannot by itself guarantee truth. Every model remains a conditional story, framed by the priors we choose and the theories we inherit. Iteration can refine these frames, but it can also entrench them. The aspiration to know what our models say must also include the humility to accept what they cannot reveal. Theory driven models, developed through iterative checking and clear communication, are more robust to misspecification, more transparent to collaborators, and more resistant to the subtle distortions of overfitting and overconfidence.
 
 In the end, the value of craft in statistical modeling lies not in merely improving benchmark metrics, but in the depth of understanding we cultivate through careful communication and justification. The Bayesian workflow reminds us that modeling is not the automation of insight, but its deliberate construction. Our workflow is a process of listening, revising, and re-articulating until the model speaks clearly. Like any craft, its worth is measured not by throughput but by fidelity: how honestly our structure reflects the world it seeks to describe. Each diagnostic, each posterior check, each refinement of a latent path is a form of attention — a small act of resistance against the flattening logic of target metrics and checklists. These constructive habits and reflective practices are the source of fulfillment in the work. __To practice modeling as craft is to reclaim pride in knowing what our models say, what they do not say, and what they imply__  -  and to find, in that discipline and skilled attention, the satisfaction of meaningful work and useful science.