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Is is easily verified that (6.20) is satisfied. Inserting the above relationships into (6.19) yields



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A few years back, this would have been unthinkable, as numerical integration represented one of the more computationally demanding tasks To carry out numerical integration ef ciently, we use logs to transform the conditional posterior in (915), and the Barry and Pace (1999) Monte Carlo estimator for the log determinant term, along with the vectorized expression for s( )2 = ( i ) = eo eo 2 i ed eo + i2 ed ed This produces a simple numerical integration problem that can be solved rapidly using Simpson s rule We arrive at the entire conditional distribution using this numerical integration approach and then produce a draw from this distribution using inversion To see how inversion works, consider that we could arrange the distribution of values in a vector sorted from low to high.

Summary





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Bugs are simple mistakes in implementing an otherwise correct algorithm. There are generally no tests that will prove the absence of bugs. However, comparing the results from multiple independent implementations of the same algorithm is likely to reveal any bugs that affect those particular results.

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There would be a large number of similar (or identical) values in the vector near the mode of the distribution and only a few similar values at the beginning and end of the vector representing the two tails of the distribution This is because the vector re ects the frequency of values in the conditional distribution Suppose that the vector of values representing the conditional distribution contained 1000 values A uniform draw from the set of integers between 1 and 1000 can be used to index into the vector representing the conditional distribution to select a value Values selected in this fashion re ect draws by inversion from the conditional distribution of Since values near the modal value will represent a majority of the values in the vector, these will most often be the resulting value drawn for , as they should be.

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ADONET uses XML to move data from the database to the application This is not XML as in the XML data type, but XML as in the actual carrier of all SQL data, just as TDS is the native binary carrier of SQL data from SQL Server XML can support all data types without prejudice because XML is a stream of bits Visual Studio applications, conversely, rely on managed data types to represent onscreen data In other words, XML adds yet another translation layer to the mix ADONET moves the data in and out of the database on an XML stream Then it must unpackage the XML stream into the desired flavor of relational data results at the application All of the data restrictions, oddities, and problems that were discussed in the ADO section also apply to the data provided to the application by ADONET.

Consequently, developers must consider the same problems, such as data loss and compatibility problems, during development Fortunately, the managed environment provides good marshaling for data types used in database management Using ADONET may introduce a small performance penalty to transport the inherently bloated XML stream and to package and unpackage that stream However, it is unlikely that the managed environment will introduce data-translation problems In fact, the expectation is that in the near future, ADONET s XML-based I/O performance will be as good as ADO s binary transport method ADONET uses NET Framework data types, rather than define its own data types, as was the case with ADO This should immediately help curb the proliferation of data type conversion errors at runtime One particularly compelling reason to favor ADONET over ADO, even in the short term, is ADO.

Implementation Progess Detail Priority High Moderate Low Unassigned Unimplemented 0 22 68 9 In Progress 10 26 9 0 Implemented 34 12 0 0

NET s better support for the new SQL Server data types, such as XML, VARCHAR(MAX), VARBINARY(MAX), and any CLR user-defined data types.

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