daviddoji/Kalman-and-Bayesian-Filters-in-Python
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Generalized discrete Bayes with likelihood.

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All my code in this chapter hard coded the computation of the
likelihood inside the update() function, where it had no business.
Also, my treatment of the likelihood was rather hand wavey. By
pulling it out of update() and maing it explicit I have created
a firm foundation for the rest of the book.
This commit is contained in:
Roger Labbe
2016-01-17 12:02:00 -08:00
parent eca26a2422
commit d0b4a1f4bc
5 changed files with 647 additions and 561 deletions
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10
code/mkf_internal.py
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@@ -100,11 +100,13 @@ def show_residual_chart():
plt.scatter ([0,1], [158.0,est_y], c='k',s=128)
plt.scatter ([1], [164.2], c='b',s=128)
plt.scatter ([1], [159], c='r', s=128)
plt.text (1.0, 158.8, "prediction ($x_t)$", ha='center',va='top',fontsize=18,color='red')
plt.text (1.0, 164.4, "measurement ($z$)",ha='center',va='bottom',fontsize=18,color='blue')
plt.text (0, 157.8, "prior estimate ($\hat{x}_{t-1}$)", ha='center', va='top',fontsize=18)
plt.text (1.05, 158.8, r"prior $(\bar{x}_t)$", ha='center',va='top',fontsize=18,color='red')
plt.text (0.5, 159.6, "prediction", ha='center',va='top',fontsize=18,color='red')
plt.text (1.0, 164.4, r"measurement ($z$)",ha='center',va='bottom',fontsize=18,color='blue')
plt.text (0, 157.8, r"posterior ($x_{t-1}$)", ha='center', va='top',fontsize=18)
plt.text (1.02, est_y-1.5, "residual", ha='left', va='center',fontsize=18)
plt.text (0.9, est_y, "new estimate ($\hat{x}_{t}$)", ha='right', va='center',fontsize=18)
plt.text (0.9, est_y, "new estimate ($x_t$)", ha='right', va='center',fontsize=18)
plt.text (0.8, est_y-0.5, "(posterior)", ha='right', va='center',fontsize=18)
plt.xlabel('time')
ax.yaxis.set_label_position("right")
plt.ylabel('state')