Lines Matching refs:model_1
204 axes = plot_trace(models.model_1, **kwargs)
239 axes = plot_trace(models.model_1)
246 axes = plot_trace(models.model_1, **kwargs)
255 plot_trace(models.model_1, **bad_kwargs)
261 ax = plot_trace(models.model_1, **{prop: ("ls", ("-", "--"))})
332 assert plot_energy(models.model_1, kind=kind)
337 plot_energy(models.model_1, kind="bad_kind")
347 assert plot_parallel(models.model_1, var_names=["mu", "tau"], norm_method=norm_method)
354 assert plot_parallel(models.model_1, var_names=var_names, norm_method="foo")
359 axjoin, _, _ = plot_joint(models.model_1, var_names=("mu", "tau"), kind=kind)
364 ax = plot_joint(models.model_1, var_names=("mu", "tau"))
376 plot_joint(models.model_1, var_names=("mu", "tau"), kind="bad_kind")
379 plot_joint(models.model_1, var_names=("mu", "tau", "eta"))
383 plot_joint(models.model_1, var_names=("mu", "tau"), ax=axes)
527 plot_kde(models.model_1)
529 plot_kde(models.model_1.posterior)
568 ax = plot_pair(models.model_1, **kwargs)
583 plot_pair(models.model_1, kind="bad_kind")
585 plot_pair(models.model_1, var_names=["mu"])
606 ax = plot_pair(models.model_1, **kwargs)
636 models.model_1,
706 models.model_1,
767 models.model_1,
790 axes = plot_ppc(models.model_1, kind="scatter", **kwargs)
806 plot_ppc(models.model_1, kind="bad_val")
808 plot_ppc(models.model_1, num_pp_samples="bad_val")
815 axes = plot_ppc(models.model_1, kind=kind, ax=ax)
828 models.model_1, ax=[ax, *ax2], flatten=[], coords={"obs_dim": [1, 2, 3]}, animated=True
831 plot_ppc(models.model_1, ax=ax2)
835 axes = plot_ppc(models.model_1)
844 axes = plot_violin(models.model_1, var_names=var_names)
850 axes = plot_violin(models.model_1, var_names="mu", ax=ax)
855 axes = plot_violin(models.model_1, var_names=["mu", "tau"], sharey=False)
872 axes = plot_autocorr(models.model_1, combined=False)
881 axes = plot_autocorr(models.model_1, combined=True)
887 axes = plot_autocorr(models.model_1, var_names=var_names, combined=True)
917 axes = plot_rank(models.model_1, **kwargs)
952 axes = plot_posterior(models.model_1, **kwargs)
967 plot_posterior(models.model_1, rope="bad_value")
969 plot_posterior(models.model_1, ref_val="bad_value")
971 plot_posterior(models.model_1, point_estimate="bad_value")
976 axes = plot_posterior(models.model_1, var_names=("mu", "tau"), point_estimate=point_estimate)
992 model_compare = compare({"Model 1": models.model_1, "Model 2": models.model_2})
1000 model_compare = compare({"Model 1": models.model_1, "Model 2": models.model_2})
1024 hdi_data = hdi(models.model_1.posterior["theta"])
1027 ax = plot_hdi(data["y"], models.model_1.posterior["theta"], **kwargs)
1049 hdi_data = hdi(models.model_1)
1092 model_dict = {"Model 1": models.model_1, "Model 2": models.model_2}
1126 model_dict = {"Model 1": multidim_models.model_1, "Model 2": multidim_models.model_2}
1147 "Model 1": waic(models.model_1, pointwise=True),
1156 "Model 1": waic(models.model_1, pointwise=True),
1165 "Model 1": waic(models.model_1, pointwise=True, scale="log"),
1173 model_dict = {"Model 1": models.model_1}
1197 khats_data = loo(models.model_1, pointwise=True)
1229 khats_data = loo(multidim_models.model_1, pointwise=True)
1254 plot_khat(models.model_1.sample_stats)
1269 idata = models.model_1
1287 idata = models.model_1
1294 idata = models.model_1
1301 idata = models.model_1
1309 idata = models.model_1
1316 idata = models.model_1
1323 idata = deepcopy(models.model_1)
1343 axes = plot_loo_pit(idata=models.model_1, y="y", **kwargs)
1350 plot_loo_pit(idata=models.model_1, y="y", ecdf=True, use_hdi=True)
1366 idata = models.model_1
1374 idata = models.model_1
1381 idata = models.model_1
1388 idata = deepcopy(models.model_1)
1411 idata = models.model_1
1440 axes = plot_bpv(models.model_1, **kwargs)
1574 idata = models.model_1
1588 idata = multidim_models.model_1
1611 idata2 = multidim_models.model_1
1625 idata1 = models.model_1
1638 idata1 = models.model_1
1749 idata2 = multidim_models.model_1