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A Comparison of Adaptive Sampling and Interpolation of 2D BRDF Subspaces
Vávra, Radomír; Filip, Jiří; Somol, P.
2013 - English
This report comprises overview of interpolation and sampling methods of Bidirectional Reflectance Distribution Function (BRDF). We analyzed 2D BRDF subspaces of eleven materials. We compared performance of five interpolation methods, three different sampling patterns, and compared twelve adaptive sampling strategies. Finally, based on knowledge of entire data we estimated sub-optimal sampling patterns and as a reference compared them with other tested sampling approaches.
Keywords:
BRDF; interpolation; sampling
Fulltext is available at external website.
A Comparison of Adaptive Sampling and Interpolation of 2D BRDF Subspaces
This report comprises overview of interpolation and sampling methods of Bidirectional Reflectance Distribution Function (BRDF). We analyzed 2D BRDF subspaces of eleven materials. We compared ...
A causal model of price and volume on market with a market maker
Šmíd, Martin; Kopa, M.
2012 - English
A model of a rational behaviour of a risk averse partially informed market maker solving multistage decision problem was proposed, implying an easily tractable and estimable stochastic model of high frequency trade and quote data process, which was subsequently successfully tested by means of data from US electronic markets.
Keywords:
market maker; rationality; price and volume process
Fulltext is available at external website.
A causal model of price and volume on market with a market maker
A model of a rational behaviour of a risk averse partially informed market maker solving multistage decision problem was proposed, implying an easily tractable and estimable stochastic model of high ...
Approximate Bayesian Recursive Estimation: On Approximation Errors
Kárný, Miroslav; Dedecius, Kamil
2012 - English
Adaptive systems rely on recursive estimation of a firmly bounded complex- ity. As a rule, they have to use an approximation of the posterior proba- bility density function (pdf), which comprises unreduced information about the estimated parameter. In recursive setting, the latest approximate pdf is updated using the learnt system model and the newest data and then ap- proximated. The fact that approximation errors may accumulate over time course is mostly neglected in the estimator design and, at most, checked ex post. The paper inspects this problem.
Keywords:
approximate estimation; adaptive systems; recursive estimation; Kullback-Leibler divergence; forgetting
Fulltext is available at external website.
Approximate Bayesian Recursive Estimation: On Approximation Errors
Adaptive systems rely on recursive estimation of a firmly bounded complex- ity. As a rule, they have to use an approximation of the posterior proba- bility density function (pdf), which comprises ...
Experiments with PID controller for fuel consumption optimization
Suzdaleva, Evgenia; Nagy, Ivan
2012 - English
This research report describes experiments performed with a Matlab simulator of a vehicle, controlled by a PID controller. The aim of control is to reduce fuel consumption and simultaneously keep the recommended safe speed.
Keywords:
Fuel consumption optimization; PID controller; recommended speed
Fulltext is available at external website.
Experiments with PID controller for fuel consumption optimization
This research report describes experiments performed with a Matlab simulator of a vehicle, controlled by a PID controller. The aim of control is to reduce fuel consumption and simultaneously keep the ...
LP relaxations and pruning for characteristic imsets
Studený, Milan
2012 - English
The geometric approach to learning BN structure is to represent it by a certain vector; a suitable such zero-one vector is the characteristic imset, which allows to reformulate the task of finding global maximum of a score over BN structures as an integer linear programming problem. The main contribution of this report is an LP relaxation of the corresponding polytope, that is, a polyhedral description of the domain of the respective integer linear programming problem.
Keywords:
learning Bayesian network structure; quality criterion; integer linear programming
Fulltext is available at external website.
LP relaxations and pruning for characteristic imsets
The geometric approach to learning BN structure is to represent it by a certain vector; a suitable such zero-one vector is the characteristic imset, which allows to reformulate the task of finding ...
Strong solutions of semilinear stochastic partial differential equations
Hofmanová, Martina
2012 - English
It it shown that stochastic parabolic equations with periodic boundary conditions driven by a finite-dimensional Wiener process have strong solutions, if the coefficients are sufficiently smooth.
Keywords:
stochastic partial differential equations; strong solutions
Fulltext is available at external website.
Strong solutions of semilinear stochastic partial differential equations
It it shown that stochastic parabolic equations with periodic boundary conditions driven by a finite-dimensional Wiener process have strong solutions, if the coefficients are sufficiently smooth.
Changes in Inflation Dynamics under Inflation Targeting? Evidence from Central European Countries
Baxa, Jaromír; Plašil, M.; Vašíček, B.
2012 - English
The purpose of this paper is to provide a novel look at the evolution of inflation dynamics in selected Central European (CE) countries. We use the lens of the New Keynesian Phillips Curve (NKPC) nested within a time-varying framework. Exploiting a time-varying regression model with stochastic volatility estimated using Bayesian techniques, we analyze both the closed and open-economy version of the NKPC. The results point to significant differences between the inflation processes in three CE countries. While inflation persistence has almost disappeared in the Czech Republic, it remains rather high in Hungary and Poland. In addition, the volatility of inflation shocks decreased quickly a few years after the adoption of inflation targeting in the Czech Republic and Poland, whereas it remains quite stable in Hungary even after ten years’ experience of inflation targeting. Our results thus suggest that the degree of anchoring of inflation expectations varies across CE coutries.
Keywords:
Bayesian model averaging; inflation dynamics; time-varying parameter model
Fulltext is available at external website.
Changes in Inflation Dynamics under Inflation Targeting? Evidence from Central European Countries
The purpose of this paper is to provide a novel look at the evolution of inflation dynamics in selected Central European (CE) countries. We use the lens of the New Keynesian Phillips Curve (NKPC) ...
Application of Sequential Monte Carlo Estimation for Early Phase of Radiation Accident
Šmídl, Václav; Hofman, Radek
2012 - English
The early phase of radiation accident is characterized by minimum number of measured data and high uncertainty in both atmospheric conditions and radiation situation. Our goal is to provide an accurate method of radiation situation assessment that is capable to respect the uncertainty and provide informative predictions of its evolution for the involved decision makers. We propose a state space model based on atmospheric dispersion model, numerical weather model with local corrections and random walk on the model corrections and release evolution. This model is highly nonlinear and is estimated using sequential Monte Carlo. Since the model is significantly more complex that previously considered models and its estimation with naive proposal densities become too computationally demanding. We propose to construct a proposal density using problem specific simplification followed by application of the Laplace approximation. Properties of the resulting estimation procedure are illustrated on a twin experiment.
Keywords:
radiation protection; dispersion modeling; particle filter
Fulltext is available at external website.
Application of Sequential Monte Carlo Estimation for Early Phase of Radiation Accident
The early phase of radiation accident is characterized by minimum number of measured data and high uncertainty in both atmospheric conditions and radiation situation. Our goal is to provide an ...
Degenerate parabolic stochastic partial differential equations
Hofmanová, Martina
2012 - English
Well-posedness of degenerate parabolic equations with stochastic forcing is studied. Existence and uniqueness of kinetic solutions is proved by means of the vanishing viscosity method.
Keywords:
stochastic parabolic equations; kinetic solutions
Fulltext is available at external website.
Degenerate parabolic stochastic partial differential equations
Well-posedness of degenerate parabolic equations with stochastic forcing is studied. Existence and uniqueness of kinetic solutions is proved by means of the vanishing viscosity method.
State estimation with missing data and bounded uncertainty
Pavelková, Lenka
2011 - English
The paper deals with two problems in the state estimation: (i) bounded uncertainty and (ii) missing measurement data. An algorithm for the state estimation of the discrete-time state space model whose uncertainties are bounded is proposed here. The algorithm also copes with situations when some data for identification are missing. The Bayesian approach is used and maximum a posteriori probability estimates are evaluated in the discrete time instants. The proposed estimation algorithm is applied to the estimation of vehicle position when incomplete data from global positioning system together with complete data from the inertial measurement unit are at disposal.
Keywords:
state-space model; filtering; bounded noise; incomplete data
Fulltext is available at external website.
State estimation with missing data and bounded uncertainty
The paper deals with two problems in the state estimation: (i) bounded uncertainty and (ii) missing measurement data. An algorithm for the state estimation of the discrete-time state space model whose ...
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