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pro vyhledávání: '"Kohn R"'
We propose a new class of financial volatility models, called the REcurrent Conditional Heteroskedastic (RECH) models, to improve both in-sample analysis and out-ofsample forecasting of the traditional conditional heteroskedastic models. In particula
Externí odkaz:
http://arxiv.org/abs/2010.13061
The Stochastic Volatility (SV) model and its variants are widely used in the financial sector while recurrent neural network (RNN) models are successfully used in many large-scale industrial applications of Deep Learning. Our article combines these t
Externí odkaz:
http://arxiv.org/abs/1906.02884
Publikováno v:
Phys. Rev. A 99, 033836 (2019)
Harmonic generation from solid surfaces is a promising tool for producing high energy attosecond pulses. We report shaping of the harmonic spectrum to achieve the bandwidth necessary for attosecond pulse generation. The shaping is demonstrated for lo
Externí odkaz:
http://arxiv.org/abs/1901.11147
This article addresses the problem of efficient Bayesian inference in dynamic systems using particle methods and makes a number of contributions. First, we develop a correlated pseudo-marginal (CPM) approach for Bayesian inference in state space (SS)
Externí odkaz:
http://arxiv.org/abs/1612.07072
Estimating copulas with discrete marginal distributions is challenging, especially in high dimensions, because computing the likelihood contribution of each observation requires evaluating $2^{J}$ terms, with $J$ the number of discrete variables. Cur
Externí odkaz:
http://arxiv.org/abs/1608.06174
The pseudo-marginal (PM) approach is increasingly used for Bayesian inference in statistical models, where the likelihood is intractable but can be estimated unbiasedly. %Examples include random effect models, state-space models and data subsampling
Externí odkaz:
http://arxiv.org/abs/1603.02485
Publikováno v:
In Journal of Mathematical Psychology June 2020 96
This article analyses a new class of advanced particle Markov chain Monte Carlo algorithms recently introduced by Andrieu, Doucet, and Holenstein (2010). We present a natural interpretation of these methods in terms of well known unbiasedness propert
Externí odkaz:
http://arxiv.org/abs/1404.5733
This paper is concerned with Bayesian inference when the likelihood is analytically intractable but can be unbiasedly estimated. We propose an annealed importance sampling procedure for estimating expectations with respect to the posterior. The propo
Externí odkaz:
http://arxiv.org/abs/1402.6035
Autor:
Giordani, P., Kohn, R.
We construct an adaptive independent Metropolis-Hastings sampler that uses a mixture of normals as a proposal distribution. To take full advantage of the potential of adaptive sampling our algorithm updates the mixture of normals frequently, starting
Externí odkaz:
http://arxiv.org/abs/0801.1864