Multivariate Normal Distributions, in Python/Numpy.

import mvn

Example application: http://en.wikipedia.org/wiki/Talk:Kalman_filter#Example_Animation

WARNING:

1. I was learning all of this as I did it, I may have made some mistakes.
2. Often I was more interested in whether I could than whether I should.
3. If it doesn't have automated tests, it probably doesn't work.

Target API

The goal is to make these probability distributions 'easy'. Not all of this works yet.

Sensor fusion

result = sensor1 & sensor2 & sensor3

Bayseian filtering:

# Linear Kalman Filter
state[t+1] = (state[t]*stm + noise) & measurment

# Unscented Kalman Filter 
state[t+1] = mvn.Mvn(stateupdate(state[t].simplex())) & measurement

# Particle Filter 
# (states are mixtures of points)
state[t+1] = (stateupdate(state[t]) & measurement).resample()

Expectation Maximization:

mix = Mixture([A,B,C])
mix = mix.fit(data)

Regression & uncertainty:

M = mvn.Mvn(data)
M[0] = 10

Projection

T = np.Matrix(...)
dist = mvn.Mvn(data)
dist*T == mvn.Mvn(data*T)

dist2d = dist[:2]

Integration

p = dist.inBox(corner_1,corner_2)

Plotting (matplotlib):

mvn.Mvn(data).plot()