Examples need adjustments for new GraphPPL

[bvdmitri]

Migration guide

Basic examples

• Coin-Toss model (@raganapa, PR update to coin toss model for GraphPPL v4 #267)
• Bayesian Linear Regression (PR fix Bayesian Linear Regression Tutorial #243)
• Kalman filtering and smoothing (@tonmalan Updating Kalman Filter Example #260)
• Predicting Bike Rental Demand (@albertpod, Update examples with new GraphPPL #258, Needs further improvements)
• Hidden Markov Model (@Sepideh-Adamiat, fix: update Hidden Markov Model Example #259)

Advanced examples

• Mountain car (@Sepideh-Adamiat, Update MountainCart example with new Graph PPL #264)
• Advanced Tutorial (@bvdmitri, Advanced Tutorial fixes #249)
• People's skills (@carbonbasedsoul, fix: update the Assessing People's Skills example #256)
• Chance-constraints @Raphael-Tresor
• CVI (@albertpod, Update examples with new GraphPPL #258)
• Global parameter optimization (@abpolym)
• GP by SDE (@HoangMHNguyen, Update GP SSM example #251)
• Infinite data stream (@Nimrais, 4.0.0 Infinite Data Stream #248)
• Nonlinear sensor fusion (@Nimrais, Update Nonlinear Sensor Fusion #253)

Problem specific

• Autoregressive (@albertpod, Update examples with new GraphPPL #258)
• Gamma mixture (@Nimrais, Update Gamma Mixture Example #254)
• Gaussian mixture (@wouterwln, Update gaussian mixture example #250)
• HGF (@bvdmitri, Fix HGF example for GraphPPL v4.0.0 #255)
• Invertible neural networks (@bartvanerp, Migration - Invertible Neural Networks Tutorial #247)
• Probit model (@Raphael-Tresor, Dev 4.0.0 examples probit #262)
• RTS vs BIFM (@HoangMHNguyen, Update RTS-BIFM to 4.0.0 #252)
• Simple nonlinear node (@abpolym, PR update to example #270)
• Universal mixtures (@bartvanerp, Migration - Universal mixtures #246)

[Nimrais]

@bvdmitri, I would like to fix Infinite Data Stream and Gamma Mixture.

[HoangMHNguyen]

@bvdmitri I choose GPSDE and RTS vs BIFM

[Sepideh-Adamiat]

@bvdmitri I choose the Mountain Car and Hidden Markov Model.

[wouterwln]

On the Mountain Car example @Sepideh-Adamiat , it might be worthy of a revision in general. The current example is, I think, an example of @ThijsvdLaar 's 2019 paper on "Simulating Active Inference Processes by Message Passing", and you can see the ideas of the paper explicitly named and coded as such. However, as an example of RxInfer, the main goal is not to demonstrate the ideas of a paper, but to demonstrate how to make an agent operate in a simple environment. Therefore I think we might need to adopt "standard" coding practices in the Mountain Car demo too (instead of creating a function that returns a callback function that reads/writes to global variables), to make clear what code specifically belongs to RxInfer and what code is just there to whip up the environment. Let me know what you guys think

[Raphael-Tresor]

@bvdmitri I would like to fix the 'Probit model' example and "Chance-constraints"

[mhidalgoaraya]

@bvdmitri I choose the Kalman Filtering and Smoothing example

[bartvanerp]

Awesome work @wouterwln and @bvdmitri ! Migration was peanuts!

[albertpod]

@wouterwln as for Mountain Car. It's a great idea. We have more people interested in this particular example, but I agree that the way it's coded up isn't pretty. Mindful of that it needs to change, I am not sure what would be the best way. AFAIR, you had an implementation of this example with RxEnvironments. Could it be something to consider?

[Sepideh-Adamiat]

@wouterwln Regarding the mountain car example: I also agree that the current version is confusing and the coding should be improved. I can start working on that. I will try to make a PR in the upcoming weeks.

[bvdmitri]

Thanks everyone! It is super helpful! There is one example left, but I'm going to close the issue, as it becomes a bit difficult to manage different branches with different fixes together with the examples branch.