Plate Notation and Datasets in TensorFlow Probability

How is the plate notation represented in probabilistic programming languages like TFP? Here are the notes: https://raw.githubusercontent.com/Ceyron/machine-learning-and-simulation/main/english/probabilistic_machine_learning/dgm_plate_notation_and_datasets.pdf

Plate notation can be abstract. In this video I want to demystify it by providing some examples of simple Directed Graphical Models. Most importantly, we will also uncover the relationship between datasets in plate notation.

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? : Check out the GitHub Repository of the channel, where I upload all the handwritten notes and source-code files (contributions are very welcome): https://github.com/Ceyron/machine-learning-and-simulation

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Timestamps:
00:00 Opening
00:20 The initial Graphical Model
01:46 Observation, Datasets and Likelihood
03:18 A different Graphical Model, the same?
04:04 The new Graphical Model in Plate Notation
05:02 A random vector
05:46 The differences of the Graphical Models
07:02 TFP: Setup
08:07 TFP: Defining and testing the first Model
11:54 TFP: Defining the second Model with plates
16:16 TFP: Likelihood using Plate Model
17:29 Why use Plate notation at all?
20:08 TFP: Implementing the advanced Model with plates
24:56 Going further with nested plates
26:59 Concluding thoughts
28:18 End-Card