Sentences with phrase «with stochastic models»

Machine learning, big data and general advances in computer science have been great in certain areas — his foundation is doing much work with stochastic models around the spread and evolution of disease — but climate change is a tougher nut to crack, Gates explained.

It appears that we need to replace determinative with stochastic models.

While the small changes in lineage transcription observed in our data set would be consistent with a stochastic model, the ES cell model described in Figure 9 would require both cross-repression and additional positive feedback loops to drive these random changes in gene expression down multiple distinct routes.

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.

Not exact matches

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.

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.

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

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.

A more sophisticated stochastic model with more time scale modes would also lead to a more or less linear relationship — as the ESMs used in the study do.

With a stochastic dynamic system model containing a hundred free parameters, do you have any idea how easy it is to get the right answer... for the * wrong * reasons?

In the second part on absolute uncertainties, a stochastic model is developed with two parts.

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.

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.

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.

First with your approach you do end up effectively having to estimate the temperature function across the globe, and this really means building a stochastic global temperature model (and deciding how linear it is etc etc).

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.

It would be interesting if the model outputs and the TLTs were related back to a stochastic emulation with specifed model parameters given estimated values and ranges.

Seven single - site statistical downscaling methods for daily temperature and precipitation, including four deterministic algorithms [analog model (ANM), quantile mapping with delta method extrapolation (QMD), cumulative distribution function transform (CDFt), and model - based recursive partitioning (MOB)-RSB- and three stochastic algorithms [generalized linear model (GLM), Conditional Density Estimation Network Creation and Evaluation (CaDENCE), and Statistical Downscaling Model — Decision Centric (SDSM — DC] are evaluated at nine stations located in the mountainous region of Iran's Midmodel (ANM), quantile mapping with delta method extrapolation (QMD), cumulative distribution function transform (CDFt), and model - based recursive partitioning (MOB)-RSB- and three stochastic algorithms [generalized linear model (GLM), Conditional Density Estimation Network Creation and Evaluation (CaDENCE), and Statistical Downscaling Model — Decision Centric (SDSM — DC] are evaluated at nine stations located in the mountainous region of Iran's Midmodel - based recursive partitioning (MOB)-RSB- and three stochastic algorithms [generalized linear model (GLM), Conditional Density Estimation Network Creation and Evaluation (CaDENCE), and Statistical Downscaling Model — Decision Centric (SDSM — DC] are evaluated at nine stations located in the mountainous region of Iran's Midmodel (GLM), Conditional Density Estimation Network Creation and Evaluation (CaDENCE), and Statistical Downscaling Model — Decision Centric (SDSM — DC] are evaluated at nine stations located in the mountainous region of Iran's MidModel — Decision Centric (SDSM — DC] are evaluated at nine stations located in the mountainous region of Iran's Midwest.

(Stepwise multiple linear regression) The polynomial models always get better with more data — and the deterministic begins to fall out where we once found stochastic.

This paper illustrates a method for operationalizing affect dynamics using a multilevel stochastic differential equation (SDE) model, and examines how those dynamics differ with age and trait - level tendencies to deploy emotion regulation strategies (reappraisal and suppression).