Dragon King Fargesia Spez. Shennongija 'Dragon King' in Norddeutschland
Dragon King --® Selektion aus natürlichen Sämlingen von F. Vaupel Höhe: 3 bis 4 m. Im Alter auch höher. Halme: Sprossen grün mit einem roten Rand. kräftig grüne Halme, größere, lanzettförmigen Blätter, sehr dichte Blattmasse. Chinese Bamboo Dreams: Fargesia Spathacea 'Dragon King' ®: Ausführliche Informationen und Bilder im Bambus-Lexikon. 'Dragon King' ®: Ausführliche Informationen und Bilder im Bambus-Lexikon. Ein unbekannter Mann, der als Dragon King bekannt war, ist ein Schurke aus den DC Comics und ein.
Übersetzung im Kontext von „dragon-king“ in Englisch-Deutsch von Reverso Context: Prophecy speaks of the Three Adulations of the Abyssal Worm; the first. Carrying a sealed message from the war-hero Dragon King to the queen, Quentin and his outlaw companion, Theido, plunge headlong into a fantastic odyssey. Ein unbekannter Mann, der als Dragon King bekannt war, ist ein Schurke aus den DC Comics und ein. Übersetzung im Kontext von „dragon-king“ in Englisch-Deutsch von Reverso Context: Prophecy speaks of the Three Adulations of the Abyssal Worm; the first. In the Hall of the Dragon King (The Dragon King Trilogy, Band 1) | Lawhead, Stephen R. | ISBN: | Kostenloser Versand für alle Bücher mit. Carrying a sealed message from the war-hero Dragon King to the queen, Quentin and his outlaw companion, Theido, plunge headlong into a fantastic odyssey. Dragon King' (Richard Tasco, R. ) Sdlg. TB TB, 37" (94 cm), Early midseason bloom. Standards magenta mauve, slightly darker toward.
Dragon King VideoRYGIN KING - TUFF (Official Music Video)
They went up against the war criminal with the help of Pat's old teammate the Shining Knight. Sir Justin was on the quest to find the missing Holy Grail.
It was revealed that the Dragon King was responsible for the death of Firebrand all those years ago. He died during the battle, though the body was never located.
Sign In Don't have an account? Start a Wiki. Related religions. Main article: Yellow Dragon. Main articles: Azure Dragon and Ao Guang. Main article: Ao Run.
Dragon King sculpture with residual traces of pigment, dated 11th—12th century, Japan. Shanghai People's Publishing House, Namespaces Article Talk.
Views Read Edit View history. Help Community portal Recent changes Upload file. Download as PDF Printable version. The unique property of power laws is that they are scale-invariant , self-similar and fractal.
This property implies that all events—both large and small—are generated by the same mechanism, and thus there will be no distinct precursors by which the largest events may be predicted.
A well-known conceptual framework for events of this type is self-organized criticality. Such concepts are compatible with the theory of the black swan.
However Taleb has also stated that considering the power law as a model instead of a model with lighter tails e.
In a variety of studies it has been found that, despite the fact that a power law models the tail of the empirical distribution well, the largest events are significantly outlying i.
Examples of this include the largest radiation release events occurring in nuclear power plant accidents, the largest city agglomeration within the sample of cities in a country, the largest crashes in financial markets, and intraday wholesale electricity prices.
Physically speaking, dragon kings may be associated with the regime changes, bifurcations , and tipping points of complex out-of-equilibrium systems.
However, it is well known that in dynamic systems, there are many precursors as the system approaches the catastrophe. Positive feedback is also a mechanism that can spawn dragon kings.
For instance, in a stampede the number of cattle running increases the level of panic which causes more cattle to run, and so on.
In human dynamics such herding and mob behavior has also been observed in crowds, stock markets, and so on see herd behavior.
Dragon kings are also caused by attractor bubbling in coupled oscillator systems. These excursions form the dragon kings, as illustrated in the figure.
It is claimed that such models can describe many real phenomena such as earthquakes, brain activity, etc. It could also be the case that dragon kings are created as a result of system control or intervention.
That is, trying to suppress the release of stress or death in dynamic complex systems may lead to an accumulation of stress or a maturation towards instability.
Such fires are inconvenient and thus we may wish that they are diligently extinguished. This leads to long periods without inconvenient fires, however, in the absence of fires, dead wood accumulates.
Once this accumulation reaches a critical point, and a fire starts, the fire becomes so large that it cannot be controlled—a singular event that could be considered to be a dragon king.
Other policies, such as doing nothing allowing for small fires to occur naturally , or performing strategic controlled burning , would avoid enormous fires by allowing for frequent small ones.
Another example is monetary policy. Quantitative easing programs and low interest rate policies are common, with the intention of avoiding recessions, promoting growth, etc.
However, such programs build instability by increasing income inequality, keeping weak firms alive, and inflating asset bubbles.
DKs are outliers by definition. However, when calling DKs outliers there is an important proviso: In standard statistics outliers are typically erroneous values and are discarded, or statistical methods are chosen that are somehow insensitive to outliers.
Contrarily, DKs are outliers that are highly informative, and should be the focus of much statistical attention.
Thus a first step is identifying DKs in historical data. Existing tests are either based on the asymptotic properties of the empirical distribution function EDF  or on an assumption about the underlying cumulative distribution function CDF of the data.
It turns out that testing for outliers relative to an exponential distribution is very general. The latter follows from the Pickands—Balkema—de Haan theorem of extreme value theory which states that a wide range of distributions asymptotically above high thresholds have exponential or power law tails.
As an aside, this is one explanation why power law tails are so common when studying extremes. To finish the point, since the natural logarithm of a power law tail is exponential, one can take the logarithm of power law data and then test for outliers relative to an exponential tail.
There are many test statistics and techniques for testing for outliers in an exponential sample. An inward test sequentially tests the largest point, then the second largest, and so on, until the first test that is not rejected i.
The number of rejected tests identifies the number of outliers. At each step the p-value for the test statistic must be computed and, if lower than some level, the test rejected.
This test has many desirable properties: It does not require that the number of outliers be specified, it is not prone to under masking and over swamping estimation of the number outliers, it is easy to implement, and the test is independent of the value of the parameter of the exponential tail.
Some examples of where dragon kings have been detected as outliers include:  . How one models and predicts dragon kings depends on the underlying mechanism.
However, the common approach will require continuous monitoring of the focal system and comparing measurements with a non-linear or complex dynamic model.
It has been proposed that the more homogeneous the system, and the stronger its interactions, the more predictable it will be. For instance, in non-linear systems with phase transitions at a critical point, it is well known that a window of predictability occurs in the neighborhood of the critical point due to precursory signs: the system recovers more slowly from perturbations, autocorrelation changes, variance increases, spatial coherence increases, etc.
For the phenomena of unsustainable growth e. In systems that are discrete scale invariant such a model is power law growth, decorated with a log-periodic function.