Sentences with phrase «interpreted as the probability»

For example, Bayesian branch - support values as used by Murphy et al. [2] should not be interpreted as probabilities that a tree - topology is correct and are known to overestimate the degree of clade support [14].

Delta often is interpreted as the probability that the option will be in - the - money by expiration.

Investment risk could be interpreted as the probability of suffering a loss, lower returns, higher volatility resulting in additional costs, etc..

The final outcome was that Doug accepted what Radford and Nullius said about how the sample measurement should be interpreted as probability, with the implication that his criticism of the calibration method is invalid.

Not exact matches

In conclusion, then, while there may have been some early Christologies that interpreted the Easter experience as «exaltation» instead of «resurrection», this would not be a surprising response to «appearances» of Jesus after the crucifixion, and so does not address the nature or the probability of these appearances nor the question of whether the exaltation interpretation somehow evolved separately from the appearance traditions.

Due to the specific features of quantum theory, this set of numbers can not in general be interpreted as actual probabilities.

The race / ethnicity estimates of marginal effects in table S5 can be interpreted as the percentage point difference in the probability of receiving an NIH R01 award between applications from white investigators (the omitted category in the regressions) and applications from investigators of a given race / ethnicity.

Averages 1 - Mode, Median and Mean Averages 2 - Which Average is Best Averages 3 - Grouped Data (Estimated Mean) Averages 4 - Mean vs Estimated Mean Bar Charts Binomial Distribution 1 - Investigation and Introduction Binomial Distribution 2 - Solving Problems Binomial Distribution 3 - Expectation and Variance Box Plots Coding Combining Data Sets Cumulative Frequency Discrete Random Variables Expectation and Variance Frequency Polygons Histograms 1 - Drawing Histograms 2 - Interpreting Interpolation - Estimating the Median Moving Averages Normal Distribution 1 - Standard Normal Normal Distribution 2 - Non-Standard Normal Distribution 3 - Backwards and Further Problems Normal Distribution 4 - Approximation for Binomial Distribution Pictograms Pie Charts 1 - Drawing Pie Charts 2 - Interpreting Product Moment Correlation Coefficient (PMCC) Poisson Distribution Probability 1 Probability 2 Probability 3 Probability 4 Probability 5 Questionnaires Regression Lines Sampling Scatter Diagrams Sets 1 - 2 sets Sets 2 - 3 sets Sets 3 - Probability Sets 4 - Conditional Probability Skewness Spearman's Rank Correlation Coefficient Standard Deviation and the Variance Stem and Leaf Diagrams Two Way Tables Probability Distribution Function NOTE: Feel free to browse my shop for more excellent free and premium resources and as always please rate and feedback, thank you.

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In the standard Black — Scholes model, one can interpret the premium of the binary option in the risk - neutral world as the expected value = probability of being in - the - money * unit, discounted to the present value.

It's just common sense that the probabilities from the Trinity study shouldn't be interpreted as forward - looking probabilities for new retirees.»

Pall et al is being interpreted, e.g. in the Guardian, as raising the possibility of legal action based on the supposed increased probability of the 2000 floods.

Interpreting causation as a deterministic relation means that if A causes B, then A must always be followed by B. Probabalistic causation means that A probabilistically causes B if A's occurrence increases the probability of B.

Jeff, risk can be interpreted as the product of what can happen times the probability of it actually happening.

I interpret the black swan idea as you being unable to asses the probabilities for (some) unknown future outcomes i.e. you place wide intervals on these probabilities.

That the «objective» Bayesian method using Jeffreys» prior will produce perfect probability matching is most easily seen as being due to the general fact that an analysis using the Jeffreys» prior is not affected by applying some monotonic transformation to the parameter (and then interpreting the results as transformed, of course).

Thus the alerts should not be interpreted as an analysis of total tree cover loss area but rather as an indication of an area that has a high probability of having experienced tree cover loss or disturbance over time.

«uncertainty with the RF of the gas» might be seized on by deniers who will wrongly interpret it as an admission of ignorance when it is really just a shorthand way of saying that a tonne of any gas has an atmospheric lifetime and impact which will depend on plenty of chance events such as where it was emitted, what the temperature was at the time, etc etc and we can compensate for imperfect information by using probability distributions.

This method does not make much sense to me, and it certainly does not appear to be compatible with the Bayesian perspective in which probability is interpreted as a degree of belief.

Until we can establish a reasonable level of internal consistency and empirical adequacy, declining to interpret model - based probabilities as decision - relevant probabilities isn't high skepticism, but scientific common sense.

If we assume two complementary hypotheses H0 and H1, an experimental outcome O, know P (O H0) and P (O H1), and have an assumed prior probability ratio P (H0) / P (H1), we can calculate the posterior probability as follows: P (H0 O) / P (H1 O) = (P (O H0) / P (O H1)-RRB-(P (H0) / P (H1)-RRB- Take logs to convert that multiplication to an addition log (P (H0 O) / P (H1 O)-RRB- = log (P (O H0) / P (O H1)-RRB- + log (P (H0) / P (H1)-RRB- and we interpret this as the confidence in H0 over H1 after the observation is equal to the evidence inherent in the result of the experiment plus our confidence before the observation.

David Budescu of Fordham University in the US reports in Nature Climate Change that when people hear the words «very likely» used to describe a 95 % chance that something is the case, they are more likely to interpret that as around 50 % probability.

On the contrary, the Court of Appeal interpreted this evidence as meaning that his return to flying was «only a strong possibility and something short of a probability» — which doesn't even sound like «more likely than not».

Thus, Bayesian probability is interpreted as a measure of the current state of knowledge.

Part V of the Act, as interpreted by the Supreme Court in CCD v. VIA, thereupon shifted the onus to the carriers to prove on a balance of probabilities that the obstacle was not «undue» and that offering a IPIF policy would cause them «undue hardship.»