Sentences with phrase «stochastic modeling of»

Katz, R.W., 2002: «Stochastic modeling of hurricane damage.»

We developed an individual farm — based stochastic model of the current UK epidemic.

Quantitative RNA ‐ FISH experiments in combination with a stochastic model of transcription reveal that antisense transcription disrupts the activity of the Set3 lysine deacetylase, thus altering the rates of sense transcript production, processing and stability.

Using a stochastic model of storm motion derived from historic tracks, this paper explores the relationship between lead time and track uncertainty for Atlantic hurricanes and the implications of this relationship for evacuation decisions.

The assertion by Tomas of the impossibility of analyzing climate science due to chaotic complexity is itself being torn to shreds by straightforward stochastic models of the climate such as manifested by the CSALT model.

I posted the comment on your blog because I thought you would be interested in the math of their stochastic model of climate fluctations in Chapter 7 and give an assesment of the their methodolgy.

Not exact matches

Estimation and filtering of hidden semi Markov models, event based filters and stochastic control Principal Investigator: Robert Elliott $ 150,000

It speaks of operations research, systems analysis, technological forecasting, information theory, game theory, simulation techniques, decision theory, Delphi method, cross-impact matrix analysis, statistical time - series, stochastic models, linear programming, input - output economics, computer based command and control systems, and so on.

The study, said George V. Nazin, a professor of physical chemistry, modeled the behavior often observed in carbon nanotube - based electronic devices, where electronic traps are induced by stochastic external charges in the immediate vicinity of the nanotubes.

His specialization is the stochastic modeling and analysis of communication networks, network control, and queuing theory.

To further test the consequence of mating preference on the evolution of menopause, we modeled the effect of mutations having delayed age of onset, using stochastic, computer simulation of a population with constant size, without pre-existing diminished fertility in females, and involving mutations that affected fertility as well as mortality.

With such a powerful dataset and toolkit, we anticipate testing the predictions of biodiversity hotspots from stochastic modelling [28]--[31], as well as mapping functional gene ecology and activities throughout the world's oceans.

Here we use a stochastic, two - sex computational model implemented by computer simulation to show how male mating preference for younger females could lead to the accumulation of mutations deleterious to female fertility and thus produce a menopausal period.

(INFERence of RNA ALignment) searches DNA sequence databases for RNA structure and sequence similarities and uses a special case of profile stochastic context - free grammars called covariance models (CMs).

Given the stochastic nature of planetary accretion, each realization of a protoplanetary disk will be modeled ~ tens of times in order to provide statistically significant outcomes that will allow us to quantify the confidence of results.

Abstract: We investigate nonequilibrium behavior in (1 +1)- dimensional stochastic field theories in the context of Ginzburg - Landau models at varying cooling rates.

Use of input - output and stochastic econometric models.

As an application of our method, we examine thermal phase mixing in the context of Ginzburg - Landau models with short - range interac... ▽ More We show how to achieve lattice - spacing independent results in numerical simulations of finite - temperature stochastic scalar field theories.

All forecasted SST series were pooled and for each calendar year the forecasted nest abundances is the model average for the ensemble of 200 simulations, essentially, deterministic models within a stochastic shell [59].

The study found that the PDMP mathematical model accurately describes the random, or stochastic, dynamics of gene expression in the non-adiabatic regime (where promoter kinetics are slow and fast averaging can not occur).

An individual - based stochastic, SIRD (susceptible - infected - recovered / dead) model was used to simulate infection through predation of infected domestic dogs, and / or wild carnivores, and direct tiger - to - tiger transmission.

Dr. Cobb suggests the use of a stochastic frontier model rather than the method we used.

A Stochastic Calculus Model of Continuous Trading: Optimal Portfolios: Stanley R. PliskaDepartment of Industrial Engineering and Management Sciences.

Pricing Callable Bonds with Stochastic Interest Rate and Stochastic Default Risk: A 3D Finite Difference Model by David Wang of Hsuan Chuang University (62K PDF)-- 10 pages — February 2005

Designing such models in the first place is complicated because prepayment rates are a path - dependent and behavioural function of the stochastic interest rate.

Although the equation can not be directly used in valuing options on LETFs, it shows the necessity of using stochastic volatility models since dynamic variance is an important component of the return of LETFs.11

These paintings paint themselves by application of very simple rules and from this process new models of experience emerge that suggest a broad consilience of form and stochastic connectivity.

There may be reason to strongly suspect that in any sufficiently complicated dynamical system model (such as climate) with stochastic parameters (e.g., exactly when and where a lightning strike starts a major wildfire or a major submarine earthquake perturbs ocean circulation in a region or a major volcanic eruption introduces stratospheric aerosols), it is almost certain that any given run of the model will have periods of significant deviation from the mean of multiple runs.

tpinlb, climate models don't «predict» year - specific phenomena arising from stochastic variation that adds «noise» to progression of climate states under the influence of persistent forcings.

Topics of potential interest: The successful OCO - 2 launch, continuing likelihood of an El Niño event this fall, predictions of the September Arctic sea ice minimum, Antarctic sea ice excursions, stochastic elements in climate models etc..

And then Pielke treated these uncertainties as model uncertainties; i.e. the variance one would expect in multiple runs of a model that employed some stochastic equations.

The model parameters are fit by treating each of the six series as a stochastic realization of the stochastic measurement process.

Our empirical analysis is based on the STIRPAT model, the stochastic version of the IPAT model, using the data of 119 countries in 1990, 1995, 2000 and 2005.

The RF time series are linked to the observations of ocean heat content and temperature change through an energy balance model and a stochastic model, using a Bayesian approach to estimate the ECS from the data.

O'Gorman, P. A., and T. Schneider, 2006: Stochastic models for the kinematics of moisture flux and condensation in homogeneous turbulent flows.

Concerning the much - needed linkages to academia, he talked about the rise of Oasis (a loss modelling framework) and explained that although open source modelling exists in the horizon, scientists producing stochastic hazard sets need to be interacting with vulnerability / damage expertise if they want to see uptake of these sets by the industry.

This model follows rather cleanly from long - term stochastic processes of geological origin.

If the model is accurate enough, then the model run with the realization of the stochastic process that most matches the future record ought to be a reasonably accurate model for the evolution the mean global temperature.

Koutsoyiannis (2011) showed that an ensemble of climate model projections is fully contained WITHIN the uncertainty envelope of traditional stochastic methods using historical data, including the Hurst phenomena... the Hurst phenomena (1951) describes the large and long excursions of natural events above and below their mean, as opposed to random processes which do not exhibit such behavior.

The program will include all aspects and methods of model development from deterministic numerics to stochastic forcing; process modelling to parametrization; observational constraints to diagnostic techniques; idealized modelling to operational forecasting and climate predictions.

A true «prediction» can't be made because the result will depend on the future volcanic eruptions and other influences on albedo, but you can run the model for each of a couple dozen stochastic processes for the future volcanic activity.

If the model would be modified to a stochastic model by adding continuously small stochastic disturbances, the effect of one single change in the initial conditions would disappear rapidly.

The climate scientists that worry about these issues don't post here (much - Jeff made a single post) so you aren't really going to see a meaningful discussion on the role of chaos or stochastic processes on climates, how that is handled in model building, and what that means in terms of model verification.

Stochastic parametrisations have significantly improved the skill of weather forecasting models, and are now used in operational forecasting centres worldwide.

In contrast, the research contributions of the hydrological community have been based on more pragmatic statistical and stochastic descriptions of natural processes, which reflect a different paradigm in both understanding and modelling natural processes....

The method combines the results of long - term atmospheric reanalyses downscaled with a stochastic statistical method and homogenized station observations to derive the meteorological forcing needed for hydrological modeling.

Edward Epstein recognized in 1969 that the atmosphere could not be completely described with a single forecast run due to inherent uncertainty, and proposed a stochastic dynamic model that produced means and variances for the state of the atmosphere.

We compare aircraft observations to modeled CH4 distributions by accounting for a) transport using the Stochastic Time - Inverted Lagrangian Transport (STILT) model driven by Weather Research and Forecasting (WRF) meteorology, b) emissions from inventories such as EDGAR and ones constructed from California - specific state and county databases, each gridded to 0.1 ° x 0.1 ° resolution, and c) spatially and temporally evolving boundary conditions such as GEOS - Chem and a NOAA aircraft profile measurement derived curtain imposed at the edge of the WRF domain.

In a system such as the climate, we can never include enough variables to describe the actual system on all relevant length scales (e.g. the butterfly effect — MICROSCOPIC perturbations grow exponentially in time to drive the system to completely different states over macroscopic time) so the best that we can often do is model it as a complex nonlinear set of ordinary differential equations with stochastic noise terms — a generalized Langevin equation or generalized Master equation, as it were — and average behaviors over what one hopes is a spanning set of butterfly - wing perturbations to assess whether or not the resulting system trajectories fill the available phase space uniformly or perhaps are restricted or constrained in some way.

Rather, while the ocean may be doing something to the surface energy budget in parts of the subpolar gyre in coupled models, its effect on the AMO is small compared to the stochastic atmospheric forcing.