This a Bayesian ICA algorithm for the linear instantaneous mixing model with additive Gaussian noise [1]. The inference problem is solved by ML-II, i.e. the sources are found by integration over the source posterior and the noise covariance and mixing matrix are found by maximization of the marginal likelihood [1]. The sufficient Statistics are estimated by either variational mean field theory with the linear response correction or by adaptive TAP mean field theory [2,3]. The mean field equations are solved by a belief propagation method [4] or sequential iteration. The computational complexity is N M^3, where N is the number of time samples and M the number of sources.
The module LSQ is for unconstrained linear least-squares fitting. It is
based upon Applied Statistics algorithm AS 274 (see comments at the start
of the module). A planar-rotation algorithm is used to update the QR-
factorization. This makes it suitable for updating regressions as more
data become available. The module contains a test for singularities which
is simpler and quicker than calculating the singular-value decomposition.
An important feature of the algorithm is that it does not square the condition
number. The matrix X X is not formed. Hence it is suitable for ill-
conditioned problems, such as fitting polynomials.
By taking advantage of the MODULE facility, it has been possible to remove
many of the arguments to routines. Apart from the new function VARPRD,
and a back-substitution routine BKSUB2 which it calls, the routines behave
as in AS 274.
unix或linux下的DNA分析軟件源碼
其功能如下
1. Edit up to 256 peptide or DNA sequences simultaneously.
2. Translates DNA->protein click next to display next frame.
3. Dot matrix plot of any 2 sequences.
4. Rudimentary amino acid Statistics (MW and amino acid percentage)
5. Saves matrix plot as PBM image format.
6. Sequence reversal.
7. Creates alignment file for highlight (below).
8. Tab key toggles editing of next sequence.
Math.NET開源數學庫
C#實現
具體功能:
- A linear algebra package, see MathNet.Numerics.LinearAlgebra.
- A sparse linear algebra package, see MathNet.Numerics.LinearAlgebra.Sparse.
- Non-uniform random generators, see MathNet.Numerics.Generators.
- Distribution fonctions, see MathNet.Numerics.Distributions.
- Statistical accumulator, see MathNet.Numerics.Statistics.
- Fourier transformations, see MathNet.Numerics.Transformations.
- Miscellaneous utilies (polynomials, rationals, collections).
The standard optimum Kalman filter demands complete
knowledge of the system parameters, the input forcing functions, and
the noise Statistics. Several adaptive methods have already been devised
to obtain the unknown information using the measurements and
the filter residuals.
Magenta Systems Internet Packet Monitoring Components are a set of Delphi components designed to capture and monitor internet packets using either raw sockets or the WinPcap device driver. Hardware permitting, ethernet packets may be captured and interpreted, and Statistics maintained about the traffic. Uses of packet monitoring include totalling internet traffic by IP address and service, monitoring external or internal IP addresses and services accessed, network diagnostics, and many other applications. The component includes two demonstration applications, one that displays raw packets, the other that totals internet traffic. The components include various filters to reduce the number of packets that need to be processed, by allowing specific IP addresses to be ignored, LAN mask to ignore local traffic, and ignore non-IP traffic such as ARP.
VerboseGC demonstrates the use of the java.lang.management API to
print the garbage collection Statistics and memory usage remotely
by connecting to a JMX agent with a JMX service URL:
service:jmx:rmi:///jndi/rmi://<hostName>:<portNum>/jmxrmi
where <hostName> is the hostname and <portNum> is the port number
to which the JMX agent will be connected.
Testbenches have become an integral part of the design process, enabling you to verify that your HDL model is sufficiently tested before implementing your design and helping you automate the design verification process. It is essential, therefore, that you have confidence your testbench is thoroughly exercising your design. Collecting code coverage Statistics during simulation helps to ensure the quality and thoroughness of your tests.
This cookbook contains a wealth of solutions to problems that SQL programmers face all the time. Recipes inside range from how to perform simple tasks, like importing external data, to ways of handling issues that are more complicated, like set algebra. Each recipe includes a discussion that explains the logic and concepts underlying the solution. The book covers audit logging, hierarchies, importing data, sets, Statistics, temporal data, and data structures.
