Hands-On Machine Learning for Algorithmic Trading

Approximate inference: stochastic versus deterministic approaches

For most models of practical relevance, it will not be possible to derive the exact posterior distribution analytically and compute the expected values for the latent parameters. The model may have too many parameters, or the posterior distribution may be too complex for an analytical solution. For continuous variables, the integrals may not have closed-form solutions, while the dimensionality of the space and the complexity of the integrand may prohibit numerical integration. For discrete variables, the marginalizations involve summing over all possible configurations of the hidden variables, and though this is always possible in principle, we often find in practice that there ...