Bayesian inference for dynamical systems

Variational Bayesian inference for partially observed stochastic dynamical systems

In this paper the variational Bayesian approximation for partially observed continuous time stochastic processes is studied. We derive an EM-like algorithm and describe its implementation. The variational Expectation step is explicitly solved using the method of conditional moment generating functions and stochastic partial differential equations. The numerical experiments demonstrate that the ...

Bayesian Inference for Nonlinear Dynamical Systems — Applications and Software Implementation

The topic of this thesis is estimation of nonlinear dynamical systems, focusing on the use of methods such as particle filtering and smoothing. There are three areas of contributions: software implementation, applications of nonlinear estimation and some theoretical extensions to existing algorithms. The common theme for all the work presented is the pyParticleEst software framework, which has ...

observational dynamical systems

چکیده در این پایاننامه ابتدا فضاهای متریک فازی را به صورت مشاهدهگرایانه بررسی میکنیم. فضاهای متریک فازی و توپولوژی تولید شده توسط این متریک معرفی شدهاند. سپس بر اساس فضاهایی که در فصل اول معرفی شدهاند آشوب توپولوژیکی، مینیمالیتی و مجموعههای متقاطع در شیوههای مختلف بررسی شده- اند. در فصل سوم مفهوم مجموعههای جاذب فازی به عنوان یک مفهوم پایهای در سیستمهای نیم-دینامیکی نسبی، تعریف شده است. ...

Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems

Many natural systems, such as neurons firing in the brain or basketball teams traversing a court, give rise to time series data with complex, nonlinear dynamics. We can gain insight into these systems by decomposing the data into segments that are each explained by simpler dynamic units. Building on switching linear dynamical systems (SLDS), we develop a model class and Bayesian inference algor...

From point estimation to Bayesian inference via dynamical systems