## Monte Carlo for x

This course is about Monte Carlo for its own
sake, though applications are used because
you can't get far without them.
Here are some pointers for Monte Carlo for
specific subjects. There are lots of
good books, so this list is not exhuastive.
- Graphics:
- Pharr and Humphreys (2010) "Physically based rendering: from theory to implementation"
- Shirley and Morley (2003) "Realistic ray tracing"

- Finance:
- Glasserman (2004) "Monte Carlo methods in financial engineering"

- Physical sciences:
- Landau and Binder (2009) "A guide to Monte Carlo simulations in statistical physics"
- Newman and Barkema (1999) "Monte Carlo methods in statistical physics"

- Statistics:
- Liu (2001): "Monte Carlo strategies in scientific computing"
- Robert and Casella (2004): "Monte Carlo statistical methods"
- Congdon (2010): "Applied Bayesian hierarchical methods"

## Uniform random numbers

- Some Books:
- Knuth (1998) "The Art of Computer Programming: Vol 2 Semi-Numerical Algorithms"
- Gentle (1998) "Random number generation and Monte Carlo Methods"
- Hellekalek and Larcher (Ed.s) (1998) "Random and
Quasi-Random Point Sets"

- Some Links:
- Digits of Pi:

## Non-uniform random numbers

## Quasi-Monte Carlo, Anova, discrepancy

## Finance related

## Other