# Common Core State Standards Activity Packet ANSWERS & EXAMPLES

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Oklahoma State Department of Education Page 1 Office of Standards and Curriculum Common Core State Standards Common Core State Standards Activity Packet ANSWERS & EXAMPLES
• relationships of congruency of triangles
• linear function for a scatter plot
• work of literature
• measures of variability for numerical data from random samples
• literary significance
• use congruence
• literature from various cultures
• common core state standards
• data analysis
• data for analysis
Publié le : mercredi 28 mars 2012
Lecture(s) : 20
Tags :
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##### Inauguration
Source : cs.unc.edu
Nombre de pages : 48
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STC Lecture Series
An Introduction to the Kalman Filter
Greg Welch and Gary Bishop
University of North Carolina at Chapel Hill
Department of Computer Science
UNC Chapel Hill Computer Science Slide 1Where We’re Going
• Introduction & Intuition
• The Discrete Kalman Filter
• A Simple Example
• Variations of the Filter
• Relevant Applications & References
UNC Chapel Hill Computer Science Slide 2The Kalman filter
• Seminal paper by R.E. Kalman, 1960
• Set of mathematical equations
• Optimal estimator
– minimum mean square error
• Versatile
predict correct
predict
– Estimation
– Filtering
– Prediction
– Fusion
UNC Chapel Hill Computer Science Slide 3Why a Kalman Filter?
• Efficient “least-squares” implementation
• Past, present and future estimation
• Estimation of missing states
• Measure of estimation quality (variance)
• Robust
– forgiving in many ways
– stable given common conditions
UNC Chapel Hill Computer Science Slide 4Some Intuition
UNC Chapel Hill Computer Science Slide 5s
s
s
First Estimate
Conditional Density Function
2
z ,
z
1
1
x = z
ˆ
2
1 1
N(z , )
1 z
1
2 2
ˆ
s =
-2 0 2 4 6 8 10 12
14
z
1
1
UNC Chapel Hill Computer Science Slide 6s
s
Second Estimate
Conditional Density Function
2
z ,
z
2
2
2
N(z , )
2 z
2
x = ...?
ˆ
2
2
ˆ
s = ...?
-2 0 2 4 6 8 10 12
14
2
UNC Chapel Hill Computer Science Slide 7s
s
s
s
s
s
s
-
s
s
Combine Estimates
2 2 2 2 2 2
= + z+ + z
x
ˆ
() ()
[][]
z z z 1 z z z 2
2 2 1 2 1 1 2
= x + K z x
ˆ ˆ
[]
1 2 2 1
where
2 2 2
+
K =
()
z z z
2
1 1 2
UNC Chapel Hill Computer Science Slide 8s
s
s
Combine Variances
2 2 2
1 = 1 +1
()()
2
z z
1 2
UNC Chapel Hill Computer Science Slide 9s
s
Combined Estimate Density
Conditional Density Function
2
N(x,
)
ˆ
ˆ
x = x
ˆ ˆ
2
2 2
ˆ
s =
2
-2 0 2 4 6 8 10 12
14
UNC Chapel Hill Computer Science Slide 10

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