def fit(self, data, random_state: int=42, num_warmup: int=500, num_samples: int=2000, mcmc_kwargs: Optional[Dict[(str, Any)]]=None, run_kwargs: Optional[Dict[(str, Any)]]=None):
    self.player_ids = data['player_ids']
    kernel = NUTS(self._model)
    mcmc = MCMC(kernel, num_warmup=num_warmup, num_samples=num_samples, num_chains=1, progress_bar=True, **(mcmc_kwargs or {}))
    (rng_key, rng_key_predict) = random.split(random.PRNGKey(44))
    mcmc.run(rng_key, data['nplayer'], data['nmatch'], data['minutes'], data['y'], data['alpha'], **(run_kwargs or {}))
    self.samples = mcmc.get_samples()
    return self
def fit(self, data, random_state: int=42, num_warmup: int=500, num_samples: int=2000, mcmc_kwargs: Optional[Dict[(str, Any)]]=None, run_kwargs: Optional[Dict[(str, Any)]]=None):
    self.player_ids = data['player_ids']
    kernel = NUTS(self._model)
    mcmc = MCMC(kernel, num_warmup=num_warmup, num_samples=num_samples, num_chains=1, progress_bar=True, **(mcmc_kwargs or {}))
    (rng_key, rng_key_predict) = random.split(random.PRNGKey(44))
    mcmc.run(rng_key, data['nplayer'], data['nmatch'], data['minutes'], data['y'], data['alpha'], **(run_kwargs or {}))
    self.samples = mcmc.get_samples()
    return self