time-to-botec

Benchmark sampling in different programming languages
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README.md (4155B)

      1 <!--
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      3 @license Apache-2.0
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      5 Copyright (c) 2018 The Stdlib Authors.
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      7 Licensed under the Apache License, Version 2.0 (the "License");
      8 you may not use this file except in compliance with the License.
      9 You may obtain a copy of the License at
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     11    http://www.apache.org/licenses/LICENSE-2.0
     12 
     13 Unless required by applicable law or agreed to in writing, software
     14 distributed under the License is distributed on an "AS IS" BASIS,
     15 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     16 See the License for the specific language governing permissions and
     17 limitations under the License.
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     19 -->
     20 
     21 # Logarithm of Probability Density Function
     22 
     23 > [Uniform][uniform-distribution] distribution logarithm of [probability density function][pdf] (PDF).
     24 
     25 <section class="intro">
     26 
     27 The [probability density function][pdf] (PDF) for a [continuous uniform][uniform-distribution] random variable is
     28 
     29 <!-- <equation class="equation" label="eq:uniform_pdf" align="center" raw="f(x;a,b)=\begin{cases} \frac{1}{b - a} & \text{for } x \in [a,b] \\ 0 & \text{otherwise} \end{cases}" alt="Probability density function (PDF) for a continuous uniform distribution."> -->
     30 
     31 <div class="equation" align="center" data-raw-text="f(x;a,b)=\begin{cases} \frac{1}{b - a} &amp; \text{for } x \in [a,b] \\ 0 &amp; \text{otherwise} \end{cases}" data-equation="eq:uniform_pdf">
     32     <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@51534079fef45e990850102147e8945fb023d1d0/lib/node_modules/@stdlib/stats/base/dists/uniform/logpdf/docs/img/equation_uniform_pdf.svg" alt="Probability density function (PDF) for a continuous uniform distribution.">
     33     <br>
     34 </div>
     35 
     36 <!-- </equation> -->
     37 
     38 where `a` is the minimum support and `b` is the maximum support of the distribution. The parameters must satisfy `a < b`.
     39 
     40 </section>
     41 
     42 <!-- /.intro -->
     43 
     44 <section class="usage">
     45 
     46 ## Usage
     47 
     48 ```javascript
     49 var logpdf = require( '@stdlib/stats/base/dists/uniform/logpdf' );
     50 ```
     51 
     52 #### logpdf( x, a, b )
     53 
     54 Evaluates the logarithm of the [probability density function][pdf] (PDF) for a [continuous uniform][uniform-distribution] distribution with parameters `a` (minimum support) and `b` (maximum support).
     55 
     56 ```javascript
     57 var y = logpdf( 2.0, 0.0, 4.0 );
     58 // returns ~-1.386
     59 
     60 y = logpdf( 5.0, 0.0, 4.0 );
     61 // returns -Infinity
     62 
     63 y = logpdf( 0.25, 0.0, 1.0 );
     64 // returns 0.0
     65 ```
     66 
     67 If provided `NaN` as any argument, the function returns `NaN`.
     68 
     69 ```javascript
     70 var y = logpdf( NaN, 0.0, 1.0 );
     71 // returns NaN
     72 
     73 y = logpdf( 0.0, NaN, 1.0 );
     74 // returns NaN
     75 
     76 y = logpdf( 0.0, 0.0, NaN );
     77 // returns NaN
     78 ```
     79 
     80 If provided `a >= b`, the function returns `NaN`.
     81 
     82 ```javascript
     83 var y = logpdf( 2.5, 3.0, 2.0 );
     84 // returns NaN
     85 
     86 y = logpdf( 2.5, 3.0, 3.0 );
     87 // returns NaN
     88 ```
     89 
     90 #### logpdf.factory( a, b )
     91 
     92 Returns a `function` for evaluating the logarithm of the [PDF][pdf] of a [continuous uniform][uniform-distribution] distribution with parameters `a` (minimum support) and `b` (maximum support).
     93 
     94 ```javascript
     95 var mylogPDF = logpdf.factory( 6.0, 7.0 );
     96 var y = mylogPDF( 7.0 );
     97 // returns 0.0
     98 
     99 y = mylogPDF( 5.0 );
    100 // returns -Infinity
    101 ```
    102 
    103 </section>
    104 
    105 <!-- /.usage -->
    106 
    107 <section class="notes">
    108 
    109 ## Notes
    110 
    111 -   In virtually all cases, using the `logpdf` or `logcdf` functions is preferable to manually computing the logarithm of the `pdf` or `cdf`, respectively, since the latter is prone to overflow and underflow.
    112 
    113 </section>
    114 
    115 <!-- /.notes -->
    116 
    117 <section class="examples">
    118 
    119 ## Examples
    120 
    121 <!-- eslint no-undef: "error" -->
    122 
    123 ```javascript
    124 var randu = require( '@stdlib/random/base/randu' );
    125 var logpdf = require( '@stdlib/stats/base/dists/uniform/logpdf' );
    126 
    127 var a;
    128 var b;
    129 var x;
    130 var y;
    131 var i;
    132 
    133 for ( i = 0; i < 25; i++ ) {
    134     x = (randu() * 20.0) - 10.0;
    135     a = (randu() * 20.0) - 20.0;
    136     b = a + (randu() * 40.0);
    137     y = logpdf( x, a, b );
    138     console.log( 'x: %d, a: %d, b: %d, ln(f(x;a,b)): %d', x.toFixed( 4 ), a.toFixed( 4 ), b.toFixed( 4 ), y.toFixed( 4 ) );
    139 }
    140 ```
    141 
    142 </section>
    143 
    144 <!-- /.examples -->
    145 
    146 <section class="links">
    147 
    148 [pdf]: https://en.wikipedia.org/wiki/Probability_density_function
    149 
    150 [uniform-distribution]: https://en.wikipedia.org/wiki/Uniform_distribution_%28continuous%29
    151 
    152 </section>
    153 
    154 <!-- /.links -->