Prey Choice Model

Large-scale neural hardware systems are trending increasingly towards the “neuromimetic” architecture: a general-purpose platform that specialises the hardware for neural networks but allows flexibility in model choice. Since the model is not hard-wired into the chip, exploration of different neural and synaptic models is not merely possible but provides a rich field for research: the possibility to use the hardware to establish useful abstractions of biological neural dynamics that could lead to a functional model of neural ...

The principle that the simplest model capable of describing observed phenomena should also correspond to the best description has long been a guiding rule of inference. In this paper a Bayesian approach to formally implementing this principle is employed to develop model selection criteria for detecting structural change in financial and economic time series. Model selection criteria which allow for

There is compelling observational evidence for the existence of dark matter. Although knowledge of its underlying nature remains elusive, a variety of theories provide candidate particles [1]. Among those are supersymmetry [2] and universal extra dimensions [3], both of which predict new physics at the electro-weak scale and, in most scenarios, introduce a light, and stable (or long lived) particle that exhibits the properties of a weakly interacting massive particle (WIMP)[4]. WIMPs are an ideal dark matter candidate, predicted to have ...

This paper shows how to compute the standard errors for partial effects of exogenous firm characteristics influencing firm inefficiency under a range of popular stochastic frontier model specifications. We also develop an R2-type measure to summarize the overall explanatory power of the exogenous factors on firm inefficiency. The paper also applies a recently developed model selection procedure to choose among alternative stochastic frontier specifications using data from household maize production in Kenya. The magnitude of estimated partial effects of exogenous household characteristics on inefficiency turns out to be very sensitive to model specification, and the model selection procedure leads to an unambiguous choice of best model. We propose a bootstrapping procedure to evaluate the size and power of the model selection procedure. The empirical application also provides further evidence on how household characteristics influence technical inefficiency in maize production in developing countries.

Results obtained with two versions of the Limited Area Model (LAM) ALADIN over differently sized integration domains (large, intermediate and small) in the European area are presented in order to investigate both the general model performance and the influence of domain choice on the quality of obtained results. The aim is also to illustrate the issues related to the strategy of selection of the optimal integration domain. Each of these studies has been performed with two versions of the ALADIN model: the first one is ALADIN-CLIMATE developed at CNRM/Météo-France, the second one is ALADIN-CLIMATE/CZ prepared at the Czech Hydrometeorological Institute (CHMI). This leaves us with total of six experiments forced by the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-40 reanalysis data. The west Balkan domain covering Bulgaria is used as an evaluation region for investigation of the temporal and spatial properties of simulated precipitation and temperature fields. This region has been selected for its challenging orography making the results obtained here a valuable source for studies leading to further developments in climate modeling. It was found that size of the domain strongly affects the quality of obtained results. We have found that the largest domain reproduces the spatial characteristics of climate (such as bias) very well, but its use results in a poor representation of temporal aspects, which are however captured very well in experiments over both smaller domains. Our findings suggest that there is no optimal choice of domain size, securing the best results for both spatial and temporal evaluation. Our study also proves that model ALADIN can be efficiently used for climate research purposes, which together with its modest computational demands should make it as an attractive modeling choice for the Central and Eastern European climate research community.

It is the aim of this study to analyse how different crop growth model routines affect the simulation of water flow and nitrogen transport of a crop rotation in agricultural fields. The model system Expert-N is briefly described and used to test the crop growth sub-models against data of a six-year field experiment on sandy soils. Expert-N is a modular soil–plant–atmosphere model system, which comprises different sub-models to simulate one-dimensional vertical transport of water, solute and heat in the unsaturated zone. It includes several sub-models to describe organic matter turnover and has three generic crop growth sub-models. The latter are derived from the crop models CERES, SPASS and SUCROS. Simulations were performed using the different sub-models for each of the cereal crops in the sugar beet, winter wheat, winter barley, winter rye crop rotation. Results show the impact of crop model choice on simulated water balances and turnover of C and N. It is concluded that the simulation of root growth and plant residue mineralisation needs some improvement.

Regional economic assessments of forest management alternatives provide complex modeling problems for policy analysts. Two specific empirical complexities involve income distribution and output trade-offs within a market-based neoclassical economic framework. This paper develops a social accounting matrix (SAM) model to investigate distributional impacts and differential outputs of alternative forestland management regimes of the Upper Great Lakes forested region. Extensions to