preparing stancon

[hyunjimoon]

FiddamanMoon23_BayesSD.pdf

[hyunjimoon]

summary of our last meeting

Angie Moon, Tom Fiddaman, and Jair discussed Bayesian methods in various fields, including epidemiological modeling. Jair shared his experience of defending his PhD early February and moving to England two weeks ago. In the first part of the conversation, the speakers discussed a project proposal for an AUC conference and an applications-oriented paper highlighting the challenges and common features of their work. In the second part, they discussed workflows in system dynamics modeling, emphasizing the importance of visualizing the logic of the model to avoid errors and improve speed, and leveraging visualization tools and fostering interaction between stakeholders to improve workflows.

Transcript

https://otter.ai/u/Vtto_SL0AnYjCYyzRe3lInrQDK4?view=transcript

Action Items
[ ] Continue discussion on GitHub with abstract and outline.
[ ] Send poster on modeling workflows to Jair.
[ ] Summarize relevant examples from thesis for talk proposal.
[ ] Schedule follow up meeting to discuss SD model.
Outline
Bayesian methods for data analysis and software development.
Angie Moon expresses concern about unexpected obituaries and Tom Fiddaman shares an unexpected snowstorm experience.
Angie Moon and Tom Fiddaman discuss potential conference topic for Bayesian modeling and methodological development.
Angie Moon and Tom Fiddaman discuss applications of Bayesian statistics in epidemiological modeling, with a focus on identifiability and practical challenges.
Improving workflows for epidemiology modeling using system dynamics software.
Jair and Tom discuss the need for faster modeling workflows in system dynamics and Bayesian analysis.
Angie Moon suggests using visualization tools to validate logic and reduce errors in model building.
Tom Fiddaman agrees, emphasizing the importance of interactivity and exploratory capabilities for model building.
Angie Moon and Tom Fiddaman discuss how to create an interactive epidemiology model that users can resonate with.
Using diagrams and code for modeling and selling ideas in different communities.
Jair and Tom discuss challenges in selling their product to different communities, with Jair highlighting the need to address both diagramming and coding needs.
Jair and Tom discuss the limitations of using code to model complex systems, with Jair highlighting the benefits of using diagrams for reproducibility.
Angie Moon and Tom Fiddaman discuss model size and complexity for epidemiology talks.
Angie Moon requests Jair to summarize their thesis for the proposed talk, connecting it to concrete examples from Jair's work.
Angie and Jair discuss meeting details, Tom provides advice on SD model.

This submission form asks only abstract so might be better submit after next week's meeting. @jandraor @tomfid

[hyunjimoon]

Tom, Jair, Angie

Tom Fiddaman and Angie Moon discussed improving SD model parameters with GPT and HMC, emphasizing the importance of data framework and infrastructure. They also discussed Bayesian inference and modeling in AI, including Jair's work and Nader's paper. The speakers also exchanged comments on optimization methods for Bayesian inference and prior modeling. Later, Angie and Tom discussed reality checks in modeling work, highlighting the need for replicable tests and evaluating the effectiveness of interventions through reality checks in epidemiology.

Transcript

https://otter.ai/u/APNuLVul0oAWu-l70_Vhh-7e7Fk?view=transcript

Action Items
[ ] Incorporate Tom's diagram inputs and get final check from Jair before submission [1:01:48]
[ ] Develop a representative diagram capturing the workflow and connections to inference/decision-making by Thursday [1:01:12]
[ ] Consider attending the conference in September if the submission is accepted [0:17:24]
[ ] Provide comments on the slides to iteratively improve the material [0:22:22]
Outline
Using GPT for SD model parameters with focus on data management and uncertainty.
Tom Fiddaman and Angie Moon discuss a slide deck with Jerry, with Tom questioning the outline's structure.
Angie Moon aims to provide a Python translation of SD model for GPT API connection.
Angie Moon and Tom Fiddaman discuss using PISD and GPT for SD modeling, with Jared highlighting the need for more data framework.
Bayesian inference and decision-making in epidemiology.
Angie Moon and Tom Fiddaman discuss the importance of interactive testing and deep inspection in code, with a focus on presenting their ideas in 15 minutes.
Angie Moon discusses Jairus's paper on Bayesian inference and decision support, focusing on inference methods such as HMC and anchoring.
Angie Moon discusses using Pathfinder for inference and approximation in machine learning.
Angie Moon proposes a unified framework for policy optimization and calibration in pandemic response.
Submitting a presentation to a conference, potential challenges and ideas for improvement.
Tom and Angie discuss submission options for an upcoming conference, including a 15-minute presentation and a tutorial.
Angie Moon suggests inviting simulation-based calibration team to collaborate on tutorials for SPC.
The team's submission for an SBC was not accepted due to a combination of factors, including poor marketing and a lack of readiness for the proposal.
Angie Moon and Tom Fiddaman discuss the challenges of communicating the value of a product to an unreceptive audience.
Developing a tool for policy designers and stakeholders, with a focus on interactions and feedback loops.
Angie Moon and Tom Fiddaman discuss a document for a presentation, including diagrams and slides.
Fiddaman and Moon discuss creating a hybrid chart for decision makers, prioritizing simplicity and ease of assimilation.
Tool and model builders need more insights on policy design and stakeholder outcomes to improve automation.
Angie Moon questions the relevance of the reality check feature for policy designers and stakeholders.
Using reality checks in modeling to ensure accuracy and avoid potential pitfalls.
Developing a way to record and automate repetitive tests to improve model accuracy.
Angie Moon suggests recording test data to improve understanding of model performance.
Tom Fiddaman explains the challenges of testing the effectiveness of vaccines in a simulated epidemic scenario.
Tom Fiddaman and Angie Moon discuss the potential risks of intervening in a pandemic, including the possibility of adherence fatigue leading to a worse outcome later on.
Using reality checks in Bayesian inference.
Tom Fiddaman explains how to use Reality Check with additional harvest fraction.
Tom Fiddaman explains the reality check for prevalence, expecting a 10% decrease.
Tom Fiddaman and Angie Moon discuss using soft constraints in Bayesian inference to model prior beliefs about a task's prevalence.
Angie Moon seeks input on diagram to connect workflow and Jair's paper, aiming to submit after quick check.

[tomfid]

https://mc-stan.org/events/stancon2024/

[hyunjimoon]

evidence of submitting below: https://capture.dropbox.com/rTeusGLLLEYWQqjs
Integrating Bayesian Inference and System Dynamics with Case Studies in Epidemiology.pdf