1. Gaussians Pieter Abbeel UC Berkeley EECS Many slides adapted from Thrun, Burgard and Fox, Probabilistic Robotics TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AAAAAAAAAAAAA

2. Univariate Gaussian Multivariate Gaussian Law of Total Probability Conditioning (Bayes’ rule) Disclaimer: lots of linear algebra in next few lectures. See course homepage for pointers for brushing up your linear algebra. In fact, pretty much all computations with Gaussians will be reduced to linear algebra! Outline

3. Univariate Gaussian Gaussian distribution with mean , and standard deviation  :

4. Densities integrate to one: Mean: Variance: Properties of Gaussians

5. Central limit theorem (CLT) Classical CLT: Let X 1, X 2, … be an infinite sequence of independent random variables with E X i = , E(X i -  ) 2 =  2 Define Z n = ((X 1 + … + X n ) – n  ) / (  n 1/2 ) Then for the limit of n going to infinity we have that Z n is distributed according to N(0,1) Crude statement: things that are the result of the addition of lots of small effects tend to become Gaussian.

6. Multi-variate Gaussians

7. (integral of vector = vector of integrals of each entry) (integral of matrix = matrix of integrals of each entry)

8.   = [1; 0]   = [1 0; 0 1]   = [-.5; 0]   = [1 0; 0 1]   = [-1; -1.5]   = [1 0; 0 1] Multi-variate Gaussians: examples

9.  = [0; 0]  = [1 0 ; 0 1]   = [0; 0]   = [.6 0 ; 0.6]   = [0; 0]   = [2 0 ; 0 2]

10.   = [0; 0]   = [1 0; 0 1]   = [0; 0]   = [1 0.5; 0.5 1]   = [0; 0]   = [1 0.8; 0.8 1] Multi-variate Gaussians: examples

11.   = [0; 0]   = [1 0; 0 1]   = [0; 0]   = [1 0.5; 0.5 1]   = [0; 0]   = [1 0.8; 0.8 1] Multi-variate Gaussians: examples

12.   = [0; 0]   = [1 -0.5 ; -0.5 1]   = [0; 0]   = [1 -0.8 ; -0.8 1]   = [0; 0]   = [3 0.8 ; 0.8 1] Multi-variate Gaussians: examples

13. Gaussians --   Univariate  Multivariate

14. Partitioned Multivariate Gaussian Consider a multi-variate Gaussian and partition random vector into (X, Y).

15. Partitioned Multivariate Gaussian: Dual Representation Precision matrix Straightforward to verify from (1) that: And swapping the roles of ¡ and § : (1)

16. Marginalization: p(x) = ? We integrate out over y to find the marginal: Hence we have: Note: if we had known beforehand that p(x) would be a Gaussian distribution, then we could have found the result more quickly. We would have just needed to find and, which we had available through

17. If Then Marginalization Recap

18. Self-quiz

19. Conditioning: p(x | Y = y 0 ) = ? We have Hence we have: Mean moved according to correlation and variance on measurement Covariance § XX | Y = y0 does not depend on y 0

20. If Then Conditioning Recap