Approximating intractable short ratemodel distribution with neural network
We propose an algorithm which predicts each subsequent time step relative to the previous timestep of intractable short rate model (when adjusted for drift and overall distribution of previous percentile result) and show that the method achieves superior outcomes to the unbiased estimate both on the trained dataset and different validation data.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Fixed-Form Variational Posterior Approximation through Stochastic Linear Regression
We propose a general algorithm for approximating nonstandard Bayesian posterior distributions. The algorithm minimizes the Kullback-Leibler divergence of an approximating distribution to the intractable posterior distrib…
FormregressionApproximation of Intractable Likelihood Functions in Systems Biology via Normalizing Flows
Systems biology relies on mathematical models that often involve complex and intractable likelihood functions, posing challenges for efficient inference and model selection. Generative models, such as normalizing flows, …
Model SelectionQuasi-Bayesian Nonparametric Density Estimation via Autoregressive Predictive Updates
Bayesian methods are a popular choice for statistical inference in small-data regimes due to the regularization effect induced by the prior. In the context of density estimation, the standard nonparametric Bayesian appro…
Density EstimationVariational Boosting: Iteratively Refining Posterior Approximations
We propose a black-box variational inference method to approximate intractable distributions with an increasingly rich approximating class. Our method, termed variational boosting, iteratively refines an existing variati…
Variational InferenceA Note on Non-Negative $L_1$-Approximating Polynomials
$L_1$-Approximating polynomials, i.e., polynomials that approximate indicator functions in $L_1$-norm under certain distributions, are widely used in computational learning theory. We study the existence of \textit{non-n…