SEQUENTIAL MONTE CARLO METHODS FOR OPTIMISATION OF NEURAL NETWORKS MODELS.
JFG de Freitas, M Niranjan, AH Gee and A Doucet
We propose a novel strategy for training neural networks using sequential Monte Carlo algorithms and develop an efficient hybrid gradient descent/importance resampling algorithm. This global optimisation strategy allows us to learn the probability distribution of the network weights in a sequential framework. It is well suited to applications involving on-line, nonlinear or non-stationary signal processing. We show how the new algorithms outperform extended Kalman filter training and radial basis function networks on several problems. We address the problem of pricing option contracts, traded in financial markets. In this context, the sequential algorithms are used to estimate the parameters of sequential Black-Scholes models and multi-layer perceptrons. We found that both model structures allowed us to successfully compute the price of options on the FTSE100 index. In addition, we were able to estimate the complete probability density functions of the risk-free interest rate and implied volatility on a daily basis.
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