Searched refs:res_mle (Results 1 – 4 of 4) sorted by relevance
| /dports/math/py-statsmodels/statsmodels-0.13.1/examples/python/ |
| H A D | statespace_sarimax_pymc3.py | 127 res_mle = mod.fit(disp=False) variable
128 print(res_mle.summary())
135 predict_mle = res_mle.get_prediction()
288 lines=[(k, {}, [v]) for k, v in dict(res_mle.params).items()],
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| H A D | statespace_custom_models.py | 797 res_mle = mod.fit(disp=False)
798 print(res_mle.summary())
843 "intercept": res_mle.params[0],
844 "var.e": res_mle.params[1],
845 "var.x.coeff": res_mle.params[2],
846 "var.w.coeff": res_mle.params[3],
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| /dports/math/py-statsmodels/statsmodels-0.13.1/examples/notebooks/ |
| H A D | statespace_sarimax_pymc3.ipynb | 139 "res_mle = mod.fit(disp=False)\n",
140 "print(res_mle.summary())"
156 "predict_mle = res_mle.get_prediction()\n",
327 "# For version <= 3.6 you can use lines=dict(res_mle.params) instead\n",
330 " lines=[(k, {}, [v]) for k, v in dict(res_mle.params).items()],\n",
500 "# For version <= 3.6 you can use lines=dict(res_mle.params) instead\n",
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| H A D | statespace_custom_models.ipynb | 859 "res_mle = mod.fit(disp=False)\n",
860 "print(res_mle.summary())"
935 " \"intercept\": res_mle.params[0],\n",
936 " \"var.e\": res_mle.params[1],\n",
937 " \"var.x.coeff\": res_mle.params[2],\n",
938 " \"var.w.coeff\": res_mle.params[3],\n",
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