It is an excellent introduction even for beginners

This set of 3 chapters is a good introduction to basics of Finite Element analysis.
The other 2 chanpters are :
Chapter 2

Chapter 3

Aggregation of Correlated Risk Portfolios:Models & Algorithms
by Shaun S. Wang, Ph.D

Nice introduction and article on derivatives

by Peter Carr

by Greg Welch and Gary Bishop

Michael Johannes and Nicholas Polson
This chapter develops Markov Chain Monte Carlo (MCMC) methods for Bayesian inference in continuous-time asset pricing models. The Bayesian solution to the inference problem is the distribution of parameters and latent variables conditional on observed data, and MCMC methods provide a tool for exploring these high-dimensional, complex distributions. We first provide a description of the foundations and mechanics of MCMC algorithms. This includes a discussion of the Clifford-Hammersley theorem, the Gibbs sampler, the Metropolis-Hastings algorithm, and theoretical convergence properties of MCMC algorithms. We next provide a tutorial on building CMC algorithms for a range of continuous-time asset pricing models. We include detailed examples for equity price models, option pricing models, term structure models, and regime-switching models. Finally, we discuss the issue of sequential Bayesian

Lecturer:: Torsten Kleinow

InvestorWords.com is a comprehensive financial glossary on the web, equipping individuals with the financial knowledge necessary to understand the basics of investments.

Tutorial on Quasi-Monte Carlo Simulation