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I'm a FORTRAN/NAG/SPSS/SAS/Cephes/MathCad/R user and I don't
see where the functions like dnorm(mean, sd) are in Boost.Math?
Nearly all are provided, and many more like mean, skewness, quantiles,
complements ... but Boost.Math makes full use of C++, and it looks a
bit different. But do not panic! See section on construction and the
many examples. Briefly, the distribution is constructed with the parameters
(like location and scale) (things after the | in representation like
P(X=k|n, p) or ; in a common represention of pdf f(x; μσ2). Functions like
pdf, cdf are called with the name of that distribution and the random
variate often called x or k. For example, normal
my_norm(0, 1); pdf(my_norm, 2.0);
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I'm a user of New
SAS Functions for Computing Probabilities.
You will find
the interface more familar, but to be able to select a distribution (perhaps
using a string) see the Extras/Future Directions section, and /boost/libs/math/dot_net_example/boost_math.cpp
for an example that is used to create a C# utility (that you might also
find useful): see Statistical
Distribution Explorer
.
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I'm allegic to reading manuals and prefer to learn from examples.
Fear not - you are not alone! Many examples are available for functions
and distributions. Some are referenced directly from the text. Others
can be found at \boost_latest_release\libs\math\example. If you are a
Visual Studio user, you should be able to create projects from each of
these, making sure that the Boost library is in the include directories
list.
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How do I make sure that the Boost library is in the Visual
Studio include directories list?
You can add an include
path, for example, your Boost place /boost-latest_release, for example
X:/boost_1_45_0/
if you have a separate partition X for Boost releases. Or you can use
an environment variable BOOST_ROOT set to your Boost place, and include
that. Visual Studio before 2010 provided Tools, Options, VC++ Directories
to control directories: Visual Studio 2010 instead provides property
sheets to assist. You may find it convenient to create a new one adding
\boost-latest_release; to the existing include items in $(IncludePath).
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I'm a FORTRAN/NAG/SPSS/SAS/Cephes/MathCad/R user and I don't
see where the properties like mean, median, mode, variance, skewness
of distributions are in Boost.Math?
They are all available
(if defined for the parameters with which you constructed the distribution)
via Cumulative Distribution Function,
Probability Density Function, Quantile, Hazard
Function, Cumulative Hazard Function,
mean, median,
mode, variance,
standard deviation, skewness,
kurtosis, kurtosis_excess,
range and support.
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I am a C programmer. Can I user Boost.Math with C?
Yes you can, including all the special functions, and TR1 functions like
isnan. They appear as C functions, by being declared as "extern
C".
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I am a C# (Basic? F# FORTRAN? Other CLI?) programmer. Can I
use Boost.Math with C#?
Yes you can, including all
the special functions, and TR1 functions like isnan. But you must build the Boost.Math as a dynamic library (.dll) and
compile with the /CLI option. See the boost/math/dot_net_example
folder which contains an example that builds a simple statistical distribution
app with a GUI. See Statistical
Distribution Explorer
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What these "policies" things for?
Policies are a powerful (if necessarily complex) fine-grain mechanism
that allow you to customise the behaviour of the Boost.Math library according
to your precise needs. See Policies.
But if, very probably, the default behaviour suits you, you don't need
to know more.
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I am a C user and expect to see global C-style
::errno
set for overflow/errors etc?
You can achieve what you
want - see error
handling policies and user
error handling and many examples.
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I am a C user and expect to silently return a max value for
overflow?
You (and C++ users too) can return whatever
you want on overflow - see overflow_error
and error
handling policies and several examples.
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I don't want any error message for overflow etc?
You can control exactly what happens for all the abnormal conditions,
including the values returned. See domain_error,
overflow_error error
handling policies user
error handling etc and examples.
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My environment doesn't allow and/or I don't want exceptions.
Can I still user Boost.Math?
Yes but you must customise
the error handling: see user
error handling and changing
policies defaults .
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The docs are several hundreds of pages long! Can I read the
docs off-line or on paper?
Yes - you can download the
Boost current release of most documentation as a zip of pdfs (including
Boost.Math) from Sourceforge, for example https://sourceforge.net/projects/boost/files/boost-docs/1.45.0/boost_pdf_1_45_0.tar.gz/download.
And you can print any pages you need (or even print all pages - but be
warned that there are several hundred!). Both html and pdf versions are
highly hyperlinked. The entire Boost.Math pdf can be searched with Adobe
Reader, Edit, Find ... This can often find what you seek, a partial substitute
for a full index.
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I want a compact version for an embedded application. Can I
use float precision?
Yes - by selecting RealType template
parameter as float: for example normal_distribution<float> your_normal(mean,
sd); (But double may still be used internally, so space saving may be
less that you hope for). You can also change the promotion policy, but
accuracy might be much reduced.
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I seem to get somewhat different results compared to other
programs. Why? We hope Boost.Math to be more accurate: our
priority is accuracy (over speed). See the section on accuracy. But for
evaluations that require iterations there are parameters which can change
the required accuracy. You might be able to squeeze a little more accuracy
at the cost of runtime.
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Will my program run more slowly compared to other math functions
and statistical libraries? Probably, thought not always, and
not by too much: our priority is accuracy. For most functions, making
sure you have the latest compiler version with all optimisations switched
on is the key to speed. For evaluations that require iteration, you may
be able to gain a little more speed at the expense of accuracy. See detailed
suggestions and results on performance.
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How do I handle infinity and NaNs portably?
See nonfinite fp_facets
for Facets for Floating-Point Infinities and NaNs.
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Where are the pre-built libraries?
Good news
- you probably don't need any! - just #include <boost/math/distribution_you_want>.
But in the unlikely event that you do, see building
libraries.
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I don't see the function or distribution that I want.
You could try an email to ask the authors - but no promises!