Book contents
- Frontmatter
- Dedication
- Contents
- List of figures
- List of tables
- Acknowledgements
- Part I Our approach in its context
- Part II Dealing with extreme events
- Part III Diversification and subjective views
- Part IV How we deal with exceptional events
- Part V Building Bayesian nets in practice
- 13 Applied tools
- 14 More advanced topics: elicitation
- 15 Additional more advanced topics
- 16 A real-life example: building a realistic Bayesian net
- Part VI Dealing with normal-times returns
- Part VII Working with the full distribution
- Part VIII A framework for choice
- Part IX Numerical implementation
- Part X Analysis of portfolio allocation
- Appendix I The links with the Black–Litterman approach
- References
- Index
13 - Applied tools
from Part V - Building Bayesian nets in practice
Published online by Cambridge University Press: 18 December 2013
- Frontmatter
- Dedication
- Contents
- List of figures
- List of tables
- Acknowledgements
- Part I Our approach in its context
- Part II Dealing with extreme events
- Part III Diversification and subjective views
- Part IV How we deal with exceptional events
- Part V Building Bayesian nets in practice
- 13 Applied tools
- 14 More advanced topics: elicitation
- 15 Additional more advanced topics
- 16 A real-life example: building a realistic Bayesian net
- Part VI Dealing with normal-times returns
- Part VII Working with the full distribution
- Part VIII A framework for choice
- Part IX Numerical implementation
- Part X Analysis of portfolio allocation
- Appendix I The links with the Black–Litterman approach
- References
- Index
Summary
A word of caution
Bayesian nets are a mature technology and many numerical techniques have been developed to handle them effectively. There are literally dozens of papers to deal with virtually any of the problems that one finds when one begins to use large Bayesian nets in earnest – say, the NP-hardness of the computational burden, the exponential explosion (in the number of parents) of the entries of the conditional probability tables, what to do with underspecified nets, etc. Some of the solutions are frankly quite complex. For reasons we explain below, we are lucky, because most of this sophistication is not required for our applications. So, a few simple tricks can be profitably adopted for our applications. These ‘tricks’ tend to be the simpler ones and they will make our life easier. And these simple, entry-level, techniques have the largest marginal benefit with respect to a totally naive approach to constructing Bayesian nets.
This is great, and reassuring. However, it is very important not to get carried away even by this rather limited toolkit. The reason is that, if one becomes overenthusiastic, one risks losing one of the most positive features of our approach: its intuitional appeal, its transparency, and what we call its auditability (by which we mean the ability of a non-technical intelligent person to question its inputs, its outputs and everything in between).
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- Portfolio Management under StressA Bayesian-Net Approach to Coherent Asset Allocation, pp. 147 - 164Publisher: Cambridge University PressPrint publication year: 2014