Non-perturbative effects and renormalization in non-parametric Bayesian statistical inference
نویسندگان
چکیده
The probability that a nearest neighbour random walker is at the origin on a given structure, as t → ∞, is known to scale as t−d̄/2, where d̄ is a scaling parameter which depends on the geometry of the structure. Knowledge of the parameter d̄ gives useful information concerning the properties of the system and is used in condensed matter physics, chemistry and in other areas where diffusive phenomena occur. It is accepted that d̄ = 2df/dw, for lattices with defined fractal dimension df and random walk dimension dw. However, it is possible to find examples of fractal lattices for which d̄ 6= 2df/dw. In this talk we present the analytical calculation of d̄ for a class of ”fractal trees” that do not follow the standard rule d̄ = 2df /dw.
منابع مشابه
Bayesian Nonparametric and Parametric Inference
This paper reviews Bayesian Nonparametric methods and discusses how parametric predictive densities can be constructed using nonparametric ideas.
متن کاملA Surface Water Evaporation Estimation Model Using Bayesian Belief Networks with an Application to the Persian Gulf
Evaporation phenomena is a effective climate component on water resources management and has special importance in agriculture. In this paper, Bayesian belief networks (BBNs) as a non-linear modeling technique provide an evaporation estimation method under uncertainty. As a case study, we estimated the surface water evaporation of the Persian Gulf and worked with a dataset of observations ...
متن کاملA Surface Water Evaporation Estimation Model Using Bayesian Belief Networks with an Application to the Persian Gulf
Evaporation phenomena is a effective climate component on water resources management and has special importance in agriculture. In this paper, Bayesian belief networks (BBNs) as a non-linear modeling technique provide an evaporation estimation method under uncertainty. As a case study, we estimated the surface water evaporation of the Persian Gulf and worked with a dataset of observations ...
متن کاملMarginally specified priors for non-parametric Bayesian estimation.
Prior specification for non-parametric Bayesian inference involves the difficult task of quantifying prior knowledge about a parameter of high, often infinite, dimension. A statistician is unlikely to have informed opinions about all aspects of such a parameter but will have real information about functionals of the parameter, such as the population mean or variance. The paper proposes a new fr...
متن کاملThe Family of Scale-Mixture of Skew-Normal Distributions and Its Application in Bayesian Nonlinear Regression Models
In previous studies on fitting non-linear regression models with the symmetric structure the normality is usually assumed in the analysis of data. This choice may be inappropriate when the distribution of residual terms is asymmetric. Recently, the family of scale-mixture of skew-normal distributions is the main concern of many researchers. This family includes several skewed and heavy-tailed d...
متن کامل