A model of management teams

Management;Decision Making

A Hamiltonian treatment of stimulated Brillouin scattering in nanoscale integrated waveguides

We present a multimode Hamiltonian formulation for the problem of opto-acoustic interactions in optical waveguides. We establish a Hamiltonian representation of the acoustic field and then introduce a full system with a simple opto-acoustic coupling that includes both photoelastic/electrostrictive and radiation pressure/moving boundary effects. The Heisenberg equations of motion are used to obtain coupled mode equations for quantized envelope operators for the optical and acoustic fields. We show that the coupling coefficients obtained coincide with those established earlier, but our formalism provides a much simpler demonstration of the connection between radiation pressure and moving boundary effects than in previous work [C. Wolff et al, Physical Review A 92, 013836 (2015)].Comment: 39 pages: 20 pages for main article + 19 pages supplementary information; 3 figure

Statistical Modelling of Fishing Activities in the North Atlantic

This paper deals with the issue of modeling daily catches of fishing boats in the Grand Bank fishing grounds. We have data on catches per species for a number of vessels collected by the European Union in the context of the North Atlantic Fisheries Organization. Many variables can be thought to influence the amount caught: a number of ship characteristics (such as the size of the ship, the fishing technique used, the mesh size of the nets, etc.), are obvious candidates, but one can also consider the season or the actual location of the catch. In all, our database leads to 23 possible regressors, resulting in a set of 8:4£106 possible linear regression models. Prediction of future catches and posterior inference will be based on Bayesian model averaging, using a Markov Chain Monte Carlo Model Composition (MC3) approach. Particular attention is paid to the elicitation of the prior and the prediction of catch for single and aggregated observations.

On Bayesian nonparametric modelling of two correlated distributions

In this paper, we consider the problem of modelling a pair of related distributions using Bayesian nonparametric methods. A representation of the distributions as weighted sums of distributions is derived through normalisation. This allows us to define several classes of nonparametric priors. The properties of these distributions are explored and efficient Markov chain Monte Carlo methods are developed. The methodology is illustrated on simulated data and an example concerning hospital efficiency measurement

Adaptive MC^3 and Gibbs algorithms for Bayesian Model Averaging in Linear Regression Models

The MC$^3$ (Madigan and York, 1995) and Gibbs (George and McCulloch, 1997) samplers are the most widely implemented algorithms for Bayesian Model Averaging (BMA) in linear regression models. These samplers draw a variable at random in each iteration using uniform selection probabilities and then propose to update that variable. This may be computationally inefficient if the number of variables is large and many variables are redundant. In this work, we introduce adaptive versions of these samplers that retain their simplicity in implementation and reduce the selection probabilities of the many redundant variables. The improvements in efficiency for the adaptive samplers are illustrated in real and simulated datasets

Benchmark priors for Bayesian models averaging

In contrast to a posterior analysis given a particular sampling model, posterior model probabilities in the context of model uncertainty are typically rather sensitive to the specification of the prior. In particular, 'diffuse' priors on model-specific parameters can lead to quite unexpected consequences. Here we focus on the practically relevant situation where we need to entertain a (large) number of sampling models and we have (or wish to use) little or no subjective prior information. We aim at providing an 'automatic' or 'benchmark' prior structure that can be used in such cases. We focus on the Normal linear regression model with uncertainty in the choice of regressors. We propose a partly noninformative prior structure related to a Natural Conjugate $g$-prior specification, where the amount of subjective information requested from the user is limited to the choice of a single scalar hyperparameter $g_{0j}$. The consequences of different choices for $g_{0j}$ are examined. We investigate theoretical properties, such as consistency of the implied Bayesian procedure. Links with classical information criteria are provided. In addition, we examine the finite sample implications of several choices of $g_{0j}$ in a simulation study. The use of the MC$^3$ algorithm of Madigan and York (1995), combined with efficient coding in Fortran, makes it feasible to conduct large simulations. In addition to posterior criteria, we shall also compare the predictive performance of different priors. A classic example concerning the economics of crime will also be provided and contrasted with results in the literature. The main findings of the paper will lead us to propose a 'benchmark' prior specification in a linear regression context with model uncertainty.Bayes factors, Markov chain, Monte Carlo, Posterior odds, Prior elicitation

Carbon accretion in unthinned and thinned young-growth forest stands of the Alaskan perhumid coastal temperate rainforest.

BACKGROUND: Accounting for carbon gains and losses in young-growth forests is a key part of carbon assessments. A common silvicultural practice in young forests is thinning to increase the growth rate of residual trees. However, the effect of thinning on total stand carbon stock in these stands is uncertain. In this study we used data from 284 long-term growth and yield plots to quantify the carbon stock in unthinned and thinned young growth conifer stands in the Alaskan coastal temperate rainforest. We estimated carbon stocks and carbon accretion rates for three thinning treatments (basal area removal of 47, 60, and 73 %) and a no-thin treatment across a range of productivity classes and ages. We also accounted for the carbon content in dead trees to quantify the influence of both thinning and natural mortality in unthinned stands. RESULTS: The total tree carbon stock in naturally-regenerating unthinned young-growth forests estimated as the asymptote of the accretion curve was 484 (±26) Mg C ha-1 for live and dead trees and 398 (±20) Mg C ha-1 for live trees only. The total tree carbon stock was reduced by 16, 26, and 39 % at stand age 40 y across the increasing range of basal area removal. Modeled linear carbon accretion rates of stands 40 years after treatment were not markedly different with increasing intensity of basal area removal from reference stand values of 4.45 Mg C ha-1 year-1to treatment stand values of 5.01, 4.83, and 4.68 Mg C ha-1 year-1 respectively. However, the carbon stock reduction in thinned stands compared to the stock of carbon in the unthinned plots was maintained over the entire 100 year period of observation. CONCLUSIONS: Thinning treatments in regenerating forest stands reduce forest carbon stocks, while carbon accretion rates recovered and were similar to unthinned stands. However, that the reduction of carbon stocks in thinned stands persisted for a century indicate that the unthinned treatment option is the optimal choice for short-term carbon sequestration. Other ecologically beneficial results of thinning may override the loss of carbon due to treatment. Our model estimates can be used to calculate regional carbon losses, alleviating uncertainty in calculating the carbon cost of the treatments

Learning and Teaching Psychoanalytic Psychotherapy

Book review of: AN INTRODUCTION T O PSYCHOTHERAPY Sidney Tarachow, M.D. New York: International University Press, Inc.,1963. 332 pp., $32 .5