Tournament Skill Estimator, some modelling challenges

[junpenglao]

Did you have a look at:

What to do with low acceptance probabilities?

I am currently developing a hierarchical ratings model very similar to the one here and finding it difficult to efficiently sample from the posterior of the ratings. I am using the NUTS sampler and have tried: 1.reparameterization (the non-centered version) turning up the target_accept parameter. HalfNormal Prior for omega New variables with priors. Example Data: [image] Data Prep Code: teams = np.sort(np.unique(np.concatenate([h_matches['Team 1 ID'], h_matches['Team 2 ID']]))) maps = ob…

and

Applying timeseries over periodic data

I am currently creating a model very similar to what @sbussmann is doing in terms of a rating/skill model. Let’s say a player’s skill is represented by a y= N(0,1) prior, I am now at the point where I want to add in a time-dependent variable where it is basically an AR(1) process: y_t = N(rho*y_t-1, sigma). Where t is a period in time. During this period, multiple matches would have been played as observed data. for example: t Team 1 Team 2 Winner 0 1 2 1 0 4 3 3 1 2 3 2 Thus t…

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They are both rating models.