Quantitative Analysis
Parallel Processing
Numerical Analysis
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Python for Excel
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I. Basic math.
II. Pricing and Hedging.
III. Explicit techniques.
IV. Data Analysis.
1. Time Series.
A. Time series forecasting.
B. Updating a linear forecast.
C. Kalman filter I.
D. Kalman filter II.
a. General Kalman filter problem.
b. General Kalman filter solution.
c. Convolution of normal distributions.
d. Kalman filter calculation for linear model.
e. Kalman filter in non-linear situation.
f. Unscented transformation.
E. Simultaneous equations.
2. Classical statistics.
3. Bayesian statistics.
V. Implementation tools.
VI. Basic Math II.
VII. Implementation tools II.
VIII. Bibliography
Notation. Index. Contents.

Kalman filter in non-linear situation.


n the non-linear situation we are facing a problem of calculation of the densities

MATH according to the model MATH We perform such calculation by postulating that the densities remain Gaussian and restricting our attention to the calculation of the mean and the covariance.





Notation. Index. Contents.


















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