Search for dissertations about: "Intractable Likelihood"
Showing result 1 - 5 of 8 swedish dissertations containing the words Intractable Likelihood.
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1. Learning Stochastic Nonlinear Dynamical Systems Using Non-stationary Linear Predictors
Abstract : The estimation problem of stochastic nonlinear parametric models is recognized to be very challenging due to the intractability of the likelihood function. Recently, several methods have been developed to approximate the maximum likelihood estimator and the optimal mean-square error predictor using Monte Carlo methods. READ MORE
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2. Identification of Stochastic Nonlinear Dynamical Models Using Estimating Functions
Abstract : Data-driven modeling of stochastic nonlinear systems is recognized as a very challenging problem, even when reduced to a parameter estimation problem. A main difficulty is the intractability of the likelihood function, which renders favored estimation methods, such as the maximum likelihood method, analytically intractable. READ MORE
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3. Simulation-based Inference : From Approximate Bayesian Computation and Particle Methods to Neural Density Estimation
Abstract : This doctoral thesis in computational statistics utilizes both Monte Carlo methods(approximate Bayesian computation and sequential Monte Carlo) and machine-learning methods (deep learning and normalizing flows) to develop novel algorithms for inference in implicit Bayesian models. Implicit models are those for which calculating the likelihood function is very challenging (and often impossible), but model simulation is feasible. READ MORE
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4. Sequential Monte Carlo for inference in nonlinear state space models
Abstract : Nonlinear state space models (SSMs) are a useful class of models to describe many different kinds of systems. Some examples of its applications are to model; the volatility in financial markets, the number of infected persons during an influenza epidemic and the annual number of major earthquakes around the world. READ MORE
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5. Exploiting conjugacy in state-space models with sequential Monte Carlo
Abstract : Many processes we encounter in our daily lives are dynamical systems that can be described mathematically using state-space models. Exact inference of both states and parameters in these models is, in general, intractable. READ MORE