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Quantitative Analysis
Parallel Processing
Numerical Analysis
C++ Multithreading
Python for Excel
Python Utilities
Services
Author
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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.
a. Structure of the model with unknown parameters.
b. Recursive formula for posterior joint distribution.
c. Marginal distribution of mean.
d. Marginal distribution of precision.
D. Hierarchical analysis of normal model with known variance.
V. Implementation tools.
VI. Basic Math II.
VII. Implementation tools II.
VIII. Bibliography
Notation. Index. Contents.

Estimating unknown parameters of normal distribution.


n this section we consider estimation of parameters of normal distribution in Bayesian setup. In section ( Basic idea of Bayesian analysis ) we introduce the notion of conjugate distribution. We see that gamma distribution is conjugate to normal distribution. This observation is important when seeking justification for the "variance-gamma" techniques for valuation of derivatives.




a. Structure of the model with unknown parameters.
b. Recursive formula for posterior joint distribution.
c. Marginal distribution of mean.
d. Marginal distribution of precision.

Notation. Index. Contents.


















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