In July 2022, we organized a panel discussion with Manu Martinet of Indigo Ag and Thomas Wiecki and Bill Engels of PyMC Labs to discuss a case-study of measuring effects of crop-types in an agricultural setting. The goal of the project was to identify the underlying spatial pattern and remove it in order to measure more accurately the treatment effect, which in this case are microbes which contribute to plant yield.
PyMC Labs were consultants on this project which had limited data and which used Bayesian analyses and Gaussian processes to identify the treatment effect. We demonstrate how Bayesian modeling is a powerful tool for solving problems in biotechnology.
00:00 Thomas Wiecki does PyMC introduction
02:49 Thomas introduces self
03:33 Manu Martinet introduces self
04:25 Bill Engels introduces self
05:10 Panel discussion begins
06:51 Testing crop yields on fields
08:16 How do you sell the product to farmers?
10:55 Data modeling and challenges
13:04 Goal of the project: Estimate the spatial pattern and remove it to get the treatment effect
15:20 Gaussian processes and how they are used
18:04 Spatial Gaussian Processes
19:09 Spatial effects
22:13 Examples fields to show the spatial components
24:28 Question: How does modeling the spatial component with a Guassian process compare with other simpler methods?
25:47 Question: With the Gaussian Process(GP) can you estimate the spatial scale?
28:06 Question: How does the Gaussian Process deal with latent variables?
30:08 Advantages of the a Bayesian framework
35:00 Collaboration between Indigo and PyMC Labs review
42:43 Question: What were the biggest challenges in the study?
45:29 Question: Is there any example online for PyMC based Hierarchical Gaussian Processes(GP) regression?
46:37 Question: How did the decomposition work out between signal, spatial and noise and how do you balance the confidence between what is signal and what is noise?
47:35 Question: How to effectively use Bayesian methods to substantiate product claims to regulatory bodies?
48:07 Thank you!
If you are interested in seeing what we at PyMC Labs can do for you, then please email email@example.com. We work with companies at a variety of scales and with varying levels of existing modeling capacity. We also run corporate workshop training events and can provide sessions ranging from introduction to Bayes to more advanced topics.