<story><title>Prolog for data science</title><url>https://emiruz.com/post/2023-04-30-prolog-for-data-science/</url></story><parent_chain></parent_chain><comment><author>burakemir</author><text>Logic programming offers a good foundation for anything that people call &amp;quot;rule engines.&amp;quot; Within logic programming, there is some variation on the degree of declarativeness.&lt;p&gt;Datalog is arguably the minimal core logic programming, similar to what the lambda calculus achieves for functional programming. Unfortunately, it has been forgotten outside of database and query processing realm. A resurgence has happened in recent years, as PL researchers and also industry have discovered the virtues of datalog (e.g. Flix, DataFun). My own attempt at making this more widely known is here &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;github.com&amp;#x2F;google&amp;#x2F;mangle&quot;&gt;https:&amp;#x2F;&amp;#x2F;github.com&amp;#x2F;google&amp;#x2F;mangle&lt;/a&gt;, a language from the datalog family and its implementation as a go library.&lt;p&gt;As the example shows: plain &amp;quot;rules&amp;quot; (or: plain datalog) is rarely enough to capture everything that one wants to express: the question then is, how to combine a pure declarative &amp;quot;kernel&amp;quot; with more general purpose programming (e.g. mapping a list).&lt;p&gt;PROLOG offered one answer, already in the 1980s, but I fully reject it: the fact that the writing a program in the wrong order with negation and recursion makes it non-terminating is not something we&amp;#x27;d want everyone to deal with. Datalog with stratified recursion is somewhat better, as &amp;quot;layers of rules&amp;quot; is a concept that is easy to understand.&lt;p&gt;In mainstream programming languages, the possibility of writing non-terminating programs also exists, but is rarely an issue. That is why I believe a good combination of declarative and general-purpose has to make it really easy to recognize which parts of a program are in the declarative, terminating, safe kernel and which parts require more attention from the programmer.</text></comment>
<story><title>Prolog for data science</title><url>https://emiruz.com/post/2023-04-30-prolog-for-data-science/</url></story><parent_chain></parent_chain><comment><author>henrydark</author><text>A different, also very explicit way to go about this type of problem, that also generalizes fully, is to use a Bayesian hierarchical model of a dirichlet process and sub-isotonic regressions.&lt;p&gt;Gelman et al have written a lot about this, and they have a proposed general workflow [1]&lt;p&gt;[1] Bayesian Workflow &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;arxiv.org&amp;#x2F;abs&amp;#x2F;2011.01808&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;arxiv.org&amp;#x2F;abs&amp;#x2F;2011.01808&lt;/a&gt;</text></comment>