Tutorial for Robust Gaussian Processes via Relevance Pursuit (#2707)

Summary:
This commit adds a tutorial of the robust Relevance Pursuit model, specifically using the `RobustRelevancePursuitSingleTaskGP` and the Twitter flash crash examples, as well as a synthetic regression example that showcases both usage patterns, as well as ways of analyzing the results of the model.

## Twitter Flash Crash

<img width="1007" alt="Screenshot 2025-01-29 at 1 18 10 PM" src="https://github.com/user-attachments/assets/da292e69-eb67-4f49-b6c1-cfc1583a057a" />
<img width="1009" alt="Screenshot 2025-01-29 at 1 18 24 PM" src="https://github.com/user-attachments/assets/da3b578d-18e6-4889-ba4c-34828a152cc1" />

## Synthetic Example

<img width="990" alt="Screenshot 2025-01-29 at 1 19 01 PM" src="https://github.com/user-attachments/assets/cacb09ca-d2e3-448d-9715-bfc70fc6a41a" />
<img width="1010" alt="Screenshot 2025-01-29 at 1 19 10 PM" src="https://github.com/user-attachments/assets/020596cb-096d-4923-b405-93433f95bbf2" />
<img width="1000" alt="Screenshot 2025-01-29 at 1 19 22 PM" src="https://github.com/user-attachments/assets/0f968610-c2b2-421f-9dc6-16aebf68024d" />

Pull Request resolved: #2707

Reviewed By: Balandat

Differential Revision: D68353581

Pulled By: SebastianAment

fbshipit-source-id: 61dd0259765f09bfc421df8498990752ea89b345

botorch/models/robust_relevance_pursuit_model.py

@@ -61,7 +61,6 @@
 from gpytorch.module import Module
 from torch import Tensor
 
-
 # default fractions of outliers to consider during relevance pursuit
 FRACTIONS_OF_OUTLIERS = [
     0.0,
@@ -117,8 +116,8 @@ def __init__(
             dim=dim,
             convex_parameterization=convex_parameterization,
         )
-        self.bmc_support_size: Tensor | None = None
-        self.bmc_probability: Tensor | None = None
+        self.bmc_support_sizes: Tensor | None = None
+        self.bmc_probabilities: Tensor | None = None
         self.cache_model_trace = cache_model_trace
         self.model_trace: list[SingleTaskGP] | None = None
         self.prior_mean_of_support: float = (