Sentences with phrase «simple heuristic»

From this simple heuristic, it is possible to predict with striking accuracy how students will conduct their legal research and a number of generalizations can be deduced.

A simple heuristic that can be used if markets are overheated is — when a whole new set of investors star to jump in.

Selecting your future spouse based on the recognition heuristic might be overdoing it a bit, but when overwhelmed with potential choices at a speed - dating event, supermarket aisle or restaurant menu, going with a simple heuristic is a reasonable option.

«These observations suggest that when lacking the cognitive anchor of a central food source, fox squirrels utilize a different and perhaps simpler heuristic (problem - solving approach) to simply avoid the areas where they had previously cached,» the study concludes.

Leonardo Ricotti, Lorenzo Vannozzi, Paolo Dario, Arianna Menciassi Where wall - following works: a case study of simple heuristics vs. optimal entropy exploration.

Not exact matches

Sometimes I can get around using numbers by using simple but error - prone heuristics and sometimes I will simply repeat other people's numbers without understanding them and will make mistakes.

Canadian Ice Service, 4.7 (+ / - 0.2), Heuristic / Statistical (same as June) The 2015 forecast was derived by considering a combination of methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic ice thickness extents, as well as winter Surface Air Temperature, Sea Level Pressure and vector wind anomaly patterns and trends; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice prHeuristic / Statistical (same as June) The 2015 forecast was derived by considering a combination of methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic ice thickness extents, as well as winter Surface Air Temperature, Sea Level Pressure and vector wind anomaly patterns and trends; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice prheuristic method based on observed end - of - winter Arctic ice thickness extents, as well as winter Surface Air Temperature, Sea Level Pressure and vector wind anomaly patterns and trends; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictors.

Canadian Ice Service, 4.7 (± 0.2), Heuristic / Statistical (same as June) The 2015 forecast was derived by considering a combination of methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic ice thickness extents, as well as winter Surface Air Temperature, Sea Level Pressure and vector wind anomaly patterns and trends; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice prHeuristic / Statistical (same as June) The 2015 forecast was derived by considering a combination of methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic ice thickness extents, as well as winter Surface Air Temperature, Sea Level Pressure and vector wind anomaly patterns and trends; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice prheuristic method based on observed end - of - winter Arctic ice thickness extents, as well as winter Surface Air Temperature, Sea Level Pressure and vector wind anomaly patterns and trends; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictors.

As with previous CIS contributions, the 2016 forecast was derived by considering a combination of methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic ice thickness / extent, as well as winter surface air temperature, spring ice conditions and the summer temperature forecast; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent time - series into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictors.

Reynolds (Public), 4.06 (3.49 - 4.63), Statistical / Heuristic Because the decline in extent is due to increasing ease with which open water can be revealed by declining volume, a simple method is used to predict September sea ice extent based on May sea ice volume for the Arctic Ocean from the PIOMAS model.

It's not surprising that the GCMs are (so far) incorrect in that they do not compare well to reality or each other, to the point where simple four or five component empirical / heuristic models like that of Scafetta can outperform them.

Here, we see that Steve McIntyre is able to make reliable predictions of a climate scientist's actions using the simple prediction heuristic «if the study does not end up getting published, it means that the results did not support the catastrophic man - made global warming proposition.»

Use simple, logical heuristics to guess at your hiring manager's likely title or look through employee titles on LinkedIn (or the company's own site) to identify your would - be boss.