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Quantitative Analysis
Parallel Processing
Numerical Analysis
C++ Multithreading
Python for Excel
Python Utilities
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I. Basic math.
II. Pricing and Hedging.
III. Explicit techniques.
IV. Data Analysis.
1. Time Series.
2. Classical statistics.
3. Bayesian statistics.
A. Basic idea of Bayesian analysis.
B. Estimating the mean of normal distribution with known variance.
C. Estimating unknown parameters of normal distribution.
D. Hierarchical analysis of normal model with known variance.
a. Joint posterior distribution of mean and hyperparameters.
b. Posterior distribution of mean conditionally on hyperparameters.
c. Marginal posterior distribution of hyperparameters.
i. Distribution of mu conditionally on gamma.
ii. Posterior distribution of gamma.
iii. Prior distribution for gamma.
V. Implementation tools.
VI. Basic Math II.
VII. Implementation tools II.
VIII. Bibliography
Notation. Index. Contents.

Distribution of mu conditionally on gamma.

ssume a non-informative prior for $\mu$ : MATH . The above distribution MATH may be regarded as MATH where the distribution MATH is the function of interest in this section. We replace the dependence of ( Hierarchical2 ) on the sample MATH with the dependence on the statistic MATH : MATH MATH and keep only the terms depending on $\mu$ . We conclude that MATH hence, MATH

Notation. Index. Contents.

Copyright 2007