determine the modality of the distribution and hence the number of components

Mixture models assume that the data is multi-modal and drawn from a linear combination of uni-modal distributions. The expectation–maximization (EM) algorithm is a type of iterative unsupervised learning algorithm which alternates between updating the probability density of the state variables, based on model parameters (E-step) and updating the parameters by maximum likelihood estimation (M-step).The EM algorithm automatically determines the modality of the distribution and hence the number of components.A mixture model is only appropriate for use in finance if the modeler specifies which component is the most relevant for each observation.

 

Chapter 3 Bayesian Regression and Gaussian Processes

Determine the maximum unbraced length of beam to satisfy the requirement of adequate lateral support.

1. For the braced column section and the loading shown in Figure P10.20, determine the limit states for which the column should be designed. Use A992 steel 2. Design a….

Design a beam of A992 steel for the loading shown in Figure P11.2.

1.Design a beam of A992 steel for the loading shown in Figure P11.2. The compression flange bracing is provided at each concentrated load. The selected section should be such that….