Search results for "maximum"
showing 10 items of 753 documents
Boolean Models: Maximum Likelihood Estimation from Circular Clumps
1990
This paper deals with the problem of making inferences on the maximum radius and the intensity of the Poisson point process associated to a Boolean Model of circular primary grains with uniformly distributed random radii. The only sample information used is observed radii of circular clumps (DUPAC, 1980). The behaviour of maximum likelihood estimation has been evaluated by means of Monte Carlo methods.
Analysis of resources distribution in economics based on entropy
2002
We propose a new approach to the problem of e0cient resources distribution in di1erent types of economic systems. We also propose to use entropy as an indicator of the e0ciency of resources distribution. Our approach is based on methods of statistical physics in which the states of economic systems are described in terms of the density functions � (g; � ) of the variable — — — — � �
Comprehensive estimation of input signals and dynamics in biochemical reaction networks
2012
Abstract Motivation: Cellular information processing can be described mathematically using differential equations. Often, external stimulation of cells by compounds such as drugs or hormones leading to activation has to be considered. Mathematically, the stimulus is represented by a time-dependent input function. Parameters such as rate constants of the molecular interactions are often unknown and need to be estimated from experimental data, e.g. by maximum likelihood estimation. For this purpose, the input function has to be defined for all times of the integration interval. This is usually achieved by approximating the input by interpolation or smoothing of the measured data. This procedu…
Segmented mixed models with random changepoints: a maximum likelihood approach with application to treatment for depression study
2014
We present a simple and effective iterative procedure to estimate segmented mixed models in a likelihood based framework. Random effects and covariates are allowed for each model parameter, including the changepoint. The method is practical and avoids the computational burdens related to estimation of nonlinear mixed effects models. A conventional linear mixed model with proper covariates that account for the changepoints is the key to our estimating algorithm. We illustrate the method via simulations and using data from a randomized clinical trial focused on change in depressive symptoms over time which characteristically show two separate phases of change.
Mode-coupling theory for multiple decay channels
2013
We investigate the properties of a class of mode-coupling equations for the glass transition where the density mode decays into multiple relaxation channels. We prove the existence and uniqueness of the solutions for Newtonian as well as Brownian dynamics and demonstrate that they fulfill the requirements of correlation functions, in the latter case the solutions are purely relaxational. Furthermore, we construct an effective mode-coupling functional which allows to map the theory to the case of a single decay channel, such that the covariance principle found for the mode-coupling theory for simple liquids is properly generalized. This in turn allows establishing the maximum theorem stating…
Entropy flux in non-equilibrium thermodynamics
2004
Abstract An important problem in thermodynamics is the link between the entropy flux and the heat flux, for phenomena far from equilibrium. As an illustration we consider here the case of a rigid heat conductor subject to heating. The expression of the entropy flux is determined by the expressions of the evolution equations of the basic variables. It is shown that the coefficient relating entropy and heat fluxes differs far from equilibrium from the inverse of the non-equilibrium temperature θ . The particular case in which these two quantities are identical is examined in detail. A simple but intuitive physical illustration of the results is proposed. A comparison with information theory i…
An approximation to maximum likelihood estimates in reduced models
1990
SUMMARY An approximation to the maximum likelihood estimates of the parameters in a model can be obtained from the corresponding estimates and information matrices in an extended model, i.e. a model with additional parameters. The approximation is close provided that the data are consistent with the first model. Applications are described to log linear models for discrete data, to models for multivariate normal distributions with special covariance matrices and to mixed discrete-continuous models.
The MLE of the mean of the exponential distribution based on grouped data is stochastically increasing
2016
Abstract This paper refers to the problem stated by Balakrishnan et al. (2002). They proved that maximum likelihood estimator (MLE) of the exponential mean obtained from grouped samples is stochastically ordered provided that the sequence of the successive distances between inspection times is decreasing. In this paper we show that the assumption of monotonicity of the sequence of distances can be dropped.
Likelihood Inference for Gibbs Processes in the Analysis of Spatial Point Patterns
2001
Plusieurs auteurs ont propose des approximations stochastiques et non-stochastiques au MLE pour les processus de Gibbs utilises pour decrire les interactions entre deux points dans une distribution spatiale de points. Cettes approximations sont necessaires a cause de la difficulte en l'evaluation de la constante qui normalise la f.d.p., Cet article present une comparaison, parmi d'un model de Strauss, des methodes qui utilisent des approximations directes aux MLE et des methodes qui utilisent techniques de Monte Carlo de chaine de Markov. Les techniques de simulation utilisees sont le Gibbs sampler et l'algorithm de Metropolis-Hastings.
Geometric Entropies of Mixing (EOM)
2005
Trigonometric and trigonometric-algebraic entropies are introduced. Regularity increases the entropy and the maximal entropy is shown to result when a regular $n$-gon is inscribed in a circle. A regular $n$-gon circumscribing a circle gives the largest entropy reduction, or the smallest change in entropy from the state of maximum entropy which occurs in the asymptotic infinite $n$ limit. EOM are shown to correspond to minimum perimeter and maximum area in the theory of convex bodies, and can be used in the prediction of new inequalities for convex sets. These expressions are shown to be related to the phase functions obtained from the WKB approximation for Bessel and Hermite functions.