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		<title>Math on nyadgar.com</title>
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			<lastBuildDate>Mon, 28 Apr 2025 00:00:00 +0000</lastBuildDate>
		
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				<title>Happy Birthday Paradox</title>
				<link>https://nyadgar.com/posts/happy-birthday-paradox/</link>
				<pubDate>Mon, 28 Apr 2025 00:00:00 +0000</pubDate>
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				<description>&lt;p&gt;The Birthday &lt;em&gt;paradox&lt;/em&gt; only seems like a paradox because it unexpectedly challenges our perception&#xA;of probability. It states that in a group of only 23 individuals, there&#xA;is more than a 50% chance that at least two are sharing a birthday. Although it may seem&#xA;counterintuitive, we have the math that proves and supports this statement.&lt;/p&gt;&#xA;&lt;p&gt;&#xA;&lt;img src=&#34;cats.png&#34; style=&#34;width: 40%;&#34; alt=&#34;cats&#34;&gt;&#xA;&lt;/p&gt;&#xA;&lt;p&gt;In this article, we will gradually reprogram our intuition and build the knowledge required to&#xA;derive the expression that proves the &lt;em&gt;paradox&lt;/em&gt;. This blog focuses on software engineering and&#xA;system design so we will follow a real-world example of this problem found in software.&#xA;But first, let&amp;rsquo;s start with basic counting.&lt;/p&gt;</description>
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				<title>The Beautiful Math of Bloom Filters</title>
				<link>https://nyadgar.com/posts/the-beautiful-math-of-bloom-filters/</link>
				<pubDate>Wed, 13 Nov 2024 15:50:12 +0200</pubDate>
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				<description>&lt;p&gt;Probabilistic functions can model many algorithms and procedures. They can help us optimize&#xA;procedures to produce the best results. Experienced software engineers know that at some point, almost any&#xA;software will reach some level of non-determinism, where a solution is not absolute but approaches the best results&#xA;if using the optimal configuration. Mathematically, such a solution usually boils down to finding &lt;em&gt;minima&lt;/em&gt;, &lt;em&gt;maxima&lt;/em&gt; or &lt;em&gt;limits&lt;/em&gt;&#xA;of some probabilistic functions.&lt;/p&gt;&#xA;&lt;p&gt;In this article, we will explore the beautiful math behind Bloom filters.&#xA;We will explore the accuracy and trade-offs of Bloom filters and see why a Bloom filter can be an excellent choice for some cases,&#xA;especially in &lt;em&gt;Big Data&lt;/em&gt;, &lt;a href=&#34;https://en.wikipedia.org/wiki/Online_analytical_processing&#34;&gt;OLAP&lt;/a&gt; systems, dealing with huge and fairly static datasets.&lt;/p&gt;</description>
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