Reproduccion de piña (Ananas comosus L. Merrill) a partir de tallos e hijos en los cultivares Cayena lisa y monte lirio

Se determinó el efecto del tipo de material vegetativo y del regulador de crecimiento (Hoja verde 48 SL) sobre la brotación de yemas en los cultivares de piña (Ananas comosus L. Merrill) Cayena Lisa (CL) y Monte Lirio (ML). Se establecieron tres ensayos en arreglo de diseño de bloques completos al azar en canteros de arena. En el primer ensayo se evaluó durante tres cortes (cada 30 días) el empleo detallo entero de planta adulta(TE), tallo de planta adulta cortado longitudinalmente (TCL), tallo de planta adulta cortado transversalmente (TCT) y tallo entero de hijo (HE) en el cultivar ML; en el segundo ensayo se evaluaron TE y TCT en el cultivar CL. En el tercer ensayo se estudió la aplicación de 0.5, 1.0 y 2.0 ml l-1 agua de Hoja verde 48 SL y el testigo en el cultivar ML, durante doscortes. Se comparó la brotación de los cultivares CL y ML usando TE y TCT. A los datos de longitud (cm), grosor (cm), peso (g), número de hojas e hijos por tratamiento se les realizó análisis de varianza y separación de media (Waller-Duncan,α = 5%). En el primer ensayo no hubo diferencia significativa en número de brotes, sin embargo los provenientes TE presentaron mayor peso (8.0 y 9.3 g), grosor (1.35 cm) y longitud (8.02 cm). En el segundo ensayo hubo diferencias significativas solamente en número de brotes en el corte 1, TE produjo 2.1 brotes. TCL usando TE registró los mejores promedios en longitud, grosor, peso, número de hojas e hijos. En el tercer ensayo no hubo diferencias significativas entre los tratamientos, a excepción del corte 1 donde presentó la mayor longitud el testigo (11.60 cm). Se produjeron 800 plantas en total, la mayoría establecidas en el campo.

Forward Smoothing Using Sequential Monte Carlo

Sequential Monte Carlo (SMC) methods are a widely used set of computational tools for inference in non-linear non-Gaussian state-space models. We propose a new SMC algorithm to compute the expectation of additive functionals recursively. Essentially, it is an on-line or "forward only" implementation of a forward filtering backward smoothing SMC algorithm proposed by Doucet, Godsill and Andrieu (2000). Compared to the standard \emph{path space} SMC estimator whose asymptotic variance increases quadratically with time even under favorable mixing assumptions, the non asymptotic variance of the proposed SMC estimator only increases linearly with time. We show how this allows us to perform recursive parameter estimation using an SMC implementation of an on-line version of the Expectation-Maximization algorithm which does not suffer from the particle path degeneracy problem.

Quantum Monte Carlo study of the CO interaction with a dimer model surface for Cr(110)

The chemisorption of CO on a Cr( 110) surface is investigated using the quantum Monte Carlo method in the diffusion Monte Carlo (DMC) variant and a model Cr2CO cluster. The present results are consistent with the earlier ab initio HF study with this model that showed the tilted/ near-parallel orientation as energetically favoured over the perpendicular arrangement. The DMC energy difference between the two orientations is larger (1.9 eV) than that computed in the previous study. The distribution and reorganization of electrons during CO adsorption on the model surface are analysed using the topological electron localization function method that yields electron populations, charge transfer and clear insight on the chemical bonding that occurs with CO adsorption and dissociation on the model surface.

An overview of sequential Monte Carlo methods for parameter estimation in general state-space models

Nonlinear non-Gaussian state-space models arise in numerous applications in control and signal processing. Sequential Monte Carlo (SMC) methods, also known as Particle Filters, are numerical techniques based on Importance Sampling for solving the optimal state estimation problem. The task of calibrating the state-space model is an important problem frequently faced by practitioners and the observed data may be used to estimate the parameters of the model. The aim of this paper is to present a comprehensive overview of SMC methods that have been proposed for this task accompanied with a discussion of their advantages and limitations.

Sequential Monte Carlo computation of the score and observed information matrix in state-space models with application to parameter estimation

Sequential Monte Carlo (SMC) methods are popular computational tools for Bayesian inference in non-linear non-Gaussian state-space models. For this class of models, we propose SMC algorithms to compute the score vector and observed information matrix recursively in time. We propose two different SMC implementations, one with computational complexity $\mathcal{O}(N)$ and the other with complexity $\mathcal{O}(N^{2})$ where $N$ is the number of importance sampling draws. Although cheaper, the performance of the $\mathcal{O}(N)$ method degrades quickly in time as it inherently relies on the SMC approximation of a sequence of probability distributions whose dimension is increasing linearly with time. In particular, even under strong \textit{mixing} assumptions, the variance of the estimates computed with the $\mathcal{O}(N)$ method increases at least quadratically in time. The $\mathcal{O}(N^{2})$ is a non-standard SMC implementation that does not suffer from this rapid degrade. We then show how both methods can be used to perform batch and recursive parameter estimation.

Monte Carlo approaches to nonlinear optimal control

This paper explores the use of Monte Carlo techniques in deterministic nonlinear optimal control. Inter-dimensional population Markov Chain Monte Carlo (MCMC) techniques are proposed to solve the nonlinear optimal control problem. The linear quadratic and Acrobot problems are studied to demonstrate the successful application of the relevant techniques.

用量子Monte Carlo方法计算氢分子H_2基态(Χ~1∑_g~+)势能曲线

本文用量子MontoCarlo方法中优化试探波函数Ψ_T计算氢分子H_2基态(X~1∑_g~+)势能曲线.文中采用相当简单的波函数形式,并用固定样点优化技术优化试探波函数的参数.确定优化试探波函数后,分别用变分Monte Carlo及固定节面M0nte Carlo计算势能曲线各点能值.二种方法先后得95%和100%的相关能.因此,在量子M0nte Carlo方法中,用本文作者提出的试探波函数计算分子势能面,将会获得很好的结果.从而对分子散射和动力学的研究有重要意义.