Manual Raven: Entity

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As already mentioned, the exploration of the input space, through the initial conditions parameters affected by uncertainties, needs to be performed using the proper distribution functions. The usage of univariate distributions for sampling initial conditions is based on the assumption that the uncertain parameters are not correlated with each other. Quite often uncertain parameters are subject to correlations and thus the univariate approach is not applicable. This happens when the value of multiple initial values are not independent but statistically correlated.

The user can provide the distribution values on either Cartesian or sparse grid, which determines the interpolation algorithm used in the evaluation of the imported CDF-PDF, respectively:. As already mentioned, the sampling methods use the distributions in order to perform probability-weighted sampling of the input space. For example, in the Monte Carlo approach, a random number [0,1] is generated probability threshold and the CDF, corresponding to that probability, is inverted in order to retrieve the parameter value to be used as coordinate in the input space simulation.

The existence of the inverse for univariate distributions is guaranteed by the monotonicity of the CDF.


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As an example, the next figure shows a multivariate normal distribution for a pipe failure as function of the pressure and temperature. The plane identifies an iso-probability surface in this case, a line that represents a probability threshold of 50 percent in this example.

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Hence, the inverse of this CDF is an infinite number of points. As easily inferable, the standard sampling approach cannot directly be employed. When multivariate distributions are used, RAVEN implements a surface search algorithm for identifying the iso-probability surface location.


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    • It performs the driving of the specific sampling strategy and, hence, determines the effectiveness of the analysis, from both an accuracy and computational point of view. The DET and Adaptive samplers are less common in literature. For this reason, they are going to be explained more in details in the relative sections.

    The Forward sampler category includes all the strategies that perform the sampling of the input space without exploiting, through a dynamic learning approach, the information made available from the outcomes of calculation previously performed adaptive sampling and the common system evolution patterns that different sampled calculations can generate in the phase space dynamic event tree.

    The Dynamic Event Tree methodologies are designed to take the timing of events explicitly into account, which can save a lot of computational time and handle particular type of phenomena that are intrinsically stochastic. The main idea of this methodology is to let a system code determine the pathway of an accident scenario within a probabilistic environment. In this family of methods, a continuous monitoring of the system evolution in the phase space is needed.


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      • The motivation is that system simulations are often computationally expensive, time-consuming, and high dimensional with respect to the number of input parameters. Thus, exploring the space of all possible simulation outcomes is infeasible using finite computing resources. During simulation-based probabilistic risk analysis, it is important to discover the relationship between a potentially large number of input parameters and the output of a simulation using as few simulation trials as possible.

      Adaptive Dynamic Event Tree. Adaptive Hybrid Dynamic Event Tree. Adaptive Sobol Decomposition. Each match is awarded a 'score' based on how closely it matches the search criteria.

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      The best results are achieved by entering as much of the name as you know. For example, searching for Department of Innovation will return a much better result than entering just Innovation. The name search is fairly tolerant of spelling mistakes — e. The name search also recognises common abbreviations, spelling variations and nickname. For example Qld is equivalent to Queensland. Use advanced search to refine the results returned by a name search. For example, you can choose options to:. You can search by name or for DGR entities within a postcode. To use the DGR search option:.

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      Use the 'charity tax concessions' option to search for entities registered for charity tax concessions. You can search by name or for entities within a postcode. To use the 'charity tax concessions' search option:. Use the statistical query to report quantitative information about ABNs by location, status and type. For example, use the statistical query to report on the:. If the default option to group results to by state is selected, you may see a column labelled 'other' in the results. RunIdleOperations 1. Related in langs. Dialect PHP.

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      UserPublicSigningKey Go. Parse Go. ClassLoaderFinder Java. NetworkTileEntity Java. IndexView Python. Query public method. DocumentDatabase Class Documentation. Example 1. Show file. File: BackupRestore. Put "ayende", null, RavenJObject. IsStale ; Assert.

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      Equal 1, queryResult. Example 2. Put "ayende", null, JObject. StartBackup "raven. Dispose ; Directory. Delete "raven.

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      Restore "raven. Example 3. File: PendingRevisionsIndex.