Bayesian updating and model class selection with Subset Simulation
نویسندگان
چکیده
منابع مشابه
Bayesian Updating and Model Class Selection of Deteriorating Hysteretic Structural Models Using Seismic Response Data
Identification of structural models from measured earthquake response can play a key role in structural health monitoring, structural control and improving performance-based design. System identification using data from strong seismic shaking is complicated by the nonlinear hysteretic response of structures where the restoring forces depend on the previous time history of the structural respons...
متن کاملApproximate Bayesian Computation by Subset Simulation
A new approximate Bayesian computation (ABC) algorithm for Bayesian updating of model parameters is proposed in this paper, which combines the ABC principles with the technique of subset simulation for efficient rare-event simulation, first developed in S. K. Au and J. L. Beck [Probabilistic Engrg. Mech., 16 (2001), pp. 263–277]. It has been named ABC-SubSim. The idea is to choose the nested de...
متن کاملStochastic System Analysis and Bayesian Model Updating
Introduction: In the case that the state-space model class is nonlinear, Kalman filter and RTS smoother breaks down. Although it is always possible to linearize the nonlinear model so that Kalman filter and RTS smoother can still apply approximately, they can be not reliable. On the other hand, stochastic simulation approaches are not limited to linear model classes and can be adopted to draw s...
متن کاملCharacterizing Long-Period Ground Motions Using Bayesian Model Class Selection
National (or regional) seismic hazard maps, determined from probabilistic seismic hazard analysis, quantify the expected intensity of shaking in a region(s) over the course of one or more desired time interval(s). Building codes use these maps to define the lateral force levels for the design of new and the retrofit of existing structures. In the past, the maps depicted the shaking intensity me...
متن کاملComparing Bayesian Model Class Selection Criteria by Discrete Finite Mixtures
We investigate the problem of computing the posterior probability of a model class, given a data sample and a prior distribution for possible parameter settings. By a model class we mean a group of models which all share the same parametric form. In general this posterior may be very hard to compute for high-dimensional parameter spaces, which is usually the case with real-world applications. I...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Computer Methods in Applied Mechanics and Engineering
سال: 2017
ISSN: 0045-7825
DOI: 10.1016/j.cma.2017.01.006