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"2017-07-12T17:12:30+00:00" }, { "id": "01a0bb70-9d08-70be-8c90-890623ffa545", "title": "Monad code generation and extension", "url": "https://mathematicaforprediction.wordpress.com/2017/06/23/monad-code-generation-and-extension/", "published_at": "2017-06-23T04:18:44+00:00" }, { "id": "01a0bb70-9d08-70be-8c90-890624d0f075", "title": "Comparison of dimension reduction algorithms over mandala images generation", "url": "https://mathematicaforprediction.wordpress.com/2017/02/10/comparison-of-dimension-reduction-algorithms-over-mandala-images-generation/", "published_at": "2017-02-10T20:54:26+00:00" }, { "id": "01a0bb70-9d08-70be-8c90-890624dfc5e8", "title": "Tries with frequencies in Java", "url": "https://mathematicaforprediction.wordpress.com/2017/01/31/tries-with-frequencies-in-java/", "published_at": "2017-01-31T21:39:16+00:00" }, { "id": "01a0bb70-9d08-70be-8c90-89062510e619", "title": "Text analysis of Trump tweets", "url": 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bootstrapping, damaging, and interpolation", "url": "https://mathematicaforprediction.wordpress.com/2016/10/15/roc-for-classifier-ensembles-bootstrapping-damaging-and-interpolation/", "published_at": "2016-10-15T17:09:25+00:00" }, { "id": "01a0bb74-1e8a-732d-b46d-9e9f957e2f18", "title": "Basic example of using ROC with Linear regression", "url": "https://mathematicaforprediction.wordpress.com/2016/10/12/basic-example-of-using-roc-with-linear-regression/", "published_at": "2016-10-12T13:08:46+00:00" }, { "id": "01a0bb74-1e8a-732d-b46d-9e9f96785559", "title": "Contingency tables creation examples", "url": "https://mathematicaforprediction.wordpress.com/2016/10/04/contingency-tables-creation-examples/", "published_at": "2016-10-04T02:40:22+00:00" }, { "id": "01a0bb74-1e8a-732d-b46d-9e9f97683c7e", "title": "Adaptive numerical Lebesgue integration by set measure estimates", "url": "https://mathematicaforprediction.wordpress.com/2016/07/01/adaptive-numerical-lebesgue-integration-by-set-measure-estimates/", "published_at": "2016-07-01T19:49:02+00:00" }, { "id": "01a0bb74-1e8a-732d-b46d-9e9f979e7a48", "title": "Making Chernoff faces for data visualization", "url": "https://mathematicaforprediction.wordpress.com/2016/06/03/making-chernoff-faces-for-data-visualization/", "published_at": "2016-06-03T02:01:54+00:00" }, { "id": "01a0bb74-1e8a-732d-b46d-9e9f98651fb9", "title": "Comparison of PCA, NNMF, and ICA over image de-noising", "url": "https://mathematicaforprediction.wordpress.com/2016/05/26/comparison-of-pca-nnmf-and-ica-over-image-de-noising/", "published_at": "2016-05-26T04:23:02+00:00" }, { "id": "01a0bb74-1e8a-732d-b46d-9e9f98ec373b", "title": "Independent component analysis for multidimensional signals", "url": "https://mathematicaforprediction.wordpress.com/2016/05/23/independent-component-analysis-for-multidimensional-signals/", "published_at": "2016-05-23T23:30:11+00:00" }, { "id": "01a0bb74-1e8a-732d-b46d-9e9f99761f17", "title": "Comparison of PCA and NNMF over image de-noising", "url": "https://mathematicaforprediction.wordpress.com/2016/05/07/comparison-of-pca-and-nnmf-over-image-de-noising/", "published_at": "2016-05-07T16:42:09+00:00" }, { "id": "01a0bb74-1e8a-732d-b46d-9e9f9a65ceda", "title": "Finding outliers in 2D and 3D numerical data", "url": "https://mathematicaforprediction.wordpress.com/2016/04/30/finding-outliers-in-2d-and-3d-numerical-data/", "published_at": "2016-04-30T15:53:12+00:00" }, { "id": "01a0bb74-1e8a-732d-b46d-9e9f9b1a109f", "title": "Creating and programming domain specific languages", "url": "https://mathematicaforprediction.wordpress.com/2016/03/22/creating-and-programming-dsls/", "published_at": "2016-03-22T16:17:52+00:00" }, { "id": "01a0bb74-1e8a-732d-b46d-9e9f9bc6b5e5", "title": "Object-Oriented Design Patterns in Mathematica", "url": "https://mathematicaforprediction.wordpress.com/2016/03/20/object-oriented-design-patterns-in-mathematica/", "published_at": "2016-03-20T20:12:06+00:00" }, { "id": "01a0bb77-5bba-7169-b3c0-a2bab27715c4", "title": "UML diagrams creation and generation", "url": "https://mathematicaforprediction.wordpress.com/2016/03/14/uml-diagrams-creation-and-generation-2/", "published_at": "2016-03-14T16:03:11+00:00" }, { "id": "01a0bb77-5bba-7169-b3c0-a2bab29c1ab1", "title": "Importance of variables investigation", "url": "https://mathematicaforprediction.wordpress.com/2016/01/11/importance-of-variables-investigation/", "published_at": "2016-01-11T06:50:57+00:00" }, { "id": "01a0bb77-5bba-7169-b3c0-a2bab307701b", "title": "RSparseMatrix for sparse matrices with named rows and columns", "url": "https://mathematicaforprediction.wordpress.com/2015/10/08/rsparsematrix-for-sparse-matrices-with-named-rows-and-columns/", "published_at": "2015-10-08T05:26:29+00:00" }, { "id": "01a0bb77-5bba-7169-b3c0-a2bab3d00408", "title": "Finding local extrema in noisy data using Quantile Regression", "url": "https://mathematicaforprediction.wordpress.com/2015/09/27/finding-local-extrema-in-noisy-data-using-quantile-regression/", "published_at": "2015-09-27T17:45:45+00:00" }, { "id": "01a0bb77-5bba-7169-b3c0-a2bab405ea91", "title": "Simple time series conversational engine", "url": "https://mathematicaforprediction.wordpress.com/2014/11/29/simple-time-series-conversational-engine/", "published_at": "2014-11-29T22:39:17+00:00" }, { "id": "01a0bb77-5bba-7169-b3c0-a2bab4d9903f", "title": "Directional quantile envelopes in 3D", "url": "https://mathematicaforprediction.wordpress.com/2014/11/16/directional-quantile-envelopes-in-3d/", "published_at": "2014-11-16T21:17:26+00:00" }, { "id": "01a0bb77-5bba-7169-b3c0-a2bab4eef76e", "title": "Directional quantile envelopes", "url": "https://mathematicaforprediction.wordpress.com/2014/11/03/directional-quantile-envelopes/", "published_at": "2014-11-03T04:42:03+00:00" }, { "id": "01a0bb77-5bba-7169-b3c0-a2bab5b2c9b8", "title": "Removal of sub-trees in tries", "url": "https://mathematicaforprediction.wordpress.com/2014/10/12/removal-of-sub-trees-in-tries/", "published_at": "2014-10-12T20:49:19+00:00" }, { "id": "01a0bb77-5bba-7169-b3c0-a2bab6625734", "title": "Find Fit for Non-linear data", "url": "https://mathematicaforprediction.wordpress.com/2014/04/19/find-fit-for-non-linear-data/", "published_at": "2014-04-19T12:47:17+00:00" }, { "id": "01a0bb77-5bba-7169-b3c0-a2bab69b3aa9", "title": "Classification and association rules for census income data", "url": "https://mathematicaforprediction.wordpress.com/2014/03/30/classification-and-association-rules-for-census-income-data/", "published_at": "2014-03-30T18:01:25+00:00" }, { "id": "01a0bb7a-ac8a-71d6-902e-7048c34a3314", "title": "Enhancements of MosaicPlot", "url": "https://mathematicaforprediction.wordpress.com/2014/03/24/enhancements-of-mosaicplot/", "published_at": "2014-03-24T01:54:18+00:00" }, { "id": "01a0bb7a-ac8a-71d6-902e-7048c3f75c07", "title": "Mosaic plots for data visualization", "url": "https://mathematicaforprediction.wordpress.com/2014/03/17/mosaic-plots-for-data-visualization/", "published_at": "2014-03-17T21:15:28+00:00" }, { "id": "01a0bb7a-ac8a-71d6-902e-7048c4d414d1", "title": "Natural language processing with functional parsers", "url": "https://mathematicaforprediction.wordpress.com/2014/02/13/natural-language-processing-with-functional-parsers/", "published_at": "2014-02-13T18:16:31+00:00" }, { "id": "01a0bb7a-ac8a-71d6-902e-7048c53c243c", "title": "Classification of genome data with n-gram models", "url": "https://mathematicaforprediction.wordpress.com/2014/01/27/classification-of-genome-data-with-n-gram-models/", "published_at": "2014-01-27T14:30:27+00:00" }, { "id": "01a0bb7a-ac8a-71d6-902e-7048c612d735", "title": "Markov chains n-gram model implementation", "url": "https://mathematicaforprediction.wordpress.com/2014/01/25/markov-chains-n-gram-model-implementation/", "published_at": "2014-01-25T15:35:04+00:00" }, { "id": "01a0bb7a-ac8a-71d6-902e-7048c6a86ec0", "title": "Estimation of conditional density distributions", "url": "https://mathematicaforprediction.wordpress.com/2014/01/13/estimation-of-conditional-density-distributions/", "published_at": "2014-01-13T16:52:45+00:00" }, { "id": "01a0bb7a-ac8a-71d6-902e-7048c72854e0", "title": "OPL code for quantile regression with B-splines", "url": "https://mathematicaforprediction.wordpress.com/2014/01/10/opl-code-for-quantile-regression-with-b-splines/", "published_at": "2014-01-10T22:58:44+00:00" }, { "id": "01a0bb7a-ac8a-71d6-902e-7048c80b485d", "title": "Quantile regression with B-splines", "url": "https://mathematicaforprediction.wordpress.com/2014/01/01/quantile-regression-with-b-splines/", "published_at": "2014-01-01T00:15:21+00:00" }, { "id": "01a0bb7a-ac8a-71d6-902e-7048c82fcf81", "title": "Quantile regression robustness", "url": "https://mathematicaforprediction.wordpress.com/2013/12/23/quantile-regression-robustness/", "published_at": "2013-12-23T18:43:18+00:00" }, { "id": "01a0bb7a-ac8a-71d6-902e-7048c8d25f5c", "title": "Quantile regression through linear programming", "url": "https://mathematicaforprediction.wordpress.com/2013/12/16/quantile-regression-through-linear-programming/", "published_at": "2013-12-16T03:05:51+00:00" } ] posts Claim your blog

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2026

Jacobian conjecture counterexample by LLM original ↗

22 Jul 2026

Thin MCP Client with Docker MCP Toolkit original ↗

23 May 2026

Chatnik: LLM Host in the Shell — Part 1: First Examples & Design Principles original ↗

16 May 2026

Pi Day 2026: Formulas, Series, and Plots for π original ↗

15 Mar 2026

2025

Maze making using graphs original ↗

26 Dec 2025

Numerically 2026 is unremarkable yet happy original ↗

21 Dec 2025

Robust code generation combining grammars and LLMs original ↗

11 Dec 2025

Primitive roots generation trails original ↗

8 Apr 2025

2024

Doomsday clock parsing and plotting original ↗

31 Dec 2024

Robust LLM pipelines original ↗

22 Sept 2024

Age at creation for programming languages stats original ↗

22 May 2024

LLM над “Искусством аттриционной войны: Уроки войны России против Украины”original ↗

30 Mar 2024

LLM помогает в обработке первого интервью Карлсона-Путина original ↗

13 Feb 2024

2023

Extracting Russian casualties in Ukraine data from Mediazona publications original ↗

15 Dec 2023

Generalized Lanchester combat models original ↗

6 Dec 2023

AI vision via Wolfram Language original ↗

30 Nov 2023

Nightcore “Conflict” video making original ↗

21 Oct 2023

Wolfram Language conference in St. Petersburg original ↗

16 Jun 2023

Quantile regression 3D examples original ↗

17 May 2023

Nightcore restyling of Dolphin’s “Spring”original ↗

9 May 2023

OpenAIMode (notebook style)original ↗

2 Apr 2023

2022

Using Wolfram Engine in Raku sessions original ↗

24 Dec 2022

Halloween Rorschach animations original ↗

31 Oct 2022

Trie based classifiers evaluation original ↗

25 Aug 2022

Nightcore “Schweine” video making original ↗

9 May 2022

Re-exploring the structure of Chinese character images original ↗

3 May 2022

Random mandalas deconstruction in R, Python, and Mathematica original ↗

1 Mar 2022

2021

Cryptocurrencies data explorations original ↗

22 Jun 2021

Crypto-currencies data acquisition with visualization original ↗

19 Jun 2021

Time series search engines over COVID-19 data original ↗

27 Jan 2021

NY Times COVID-19 data visualization (Update)original ↗

15 Jan 2021

A monad for Epidemiologic Compartmental Modeling Workflows original ↗

2 Jan 2021

2020

Generation of Random Bethlehem Stars original ↗

21 Dec 2020

Making Graphs over System Dynamics Models original ↗

14 Dec 2020

How to simplify Machine learning workflows specifications? (useR! 2020)original ↗

28 Jun 2020

Investigating COVID-19 with R: data analysis and simulations original ↗

29 May 2020

Apple mobility trends data visualization (for COVID-19)original ↗

17 Apr 2020

Coronavirus propagation modeling, useR! Boston April 2020 original ↗

15 Apr 2020

SEI2HR-Econ model with quarantine and supplies scenarios original ↗

11 Apr 2020

SEI2HR model with quarantine scenarios original ↗

6 Apr 2020

Coronavirus propagation modeling tutorial presentation, OMLDS March 2020 original ↗

30 Mar 2020

NY Times COVID-19 data visualization original ↗

28 Mar 2020

WirVsVirus 2020 hackathon participation original ↗

24 Mar 2020

Scaling of epidemiology models with multi-site compartments original ↗

13 Mar 2020

Basic experiments workflow for simple epidemiological models original ↗

11 Mar 2020

Conference abstracts similarities original ↗

27 Jan 2020

Pets licensing data analysis original ↗

20 Jan 2020

2019

Wolfram Live-Coding Series on Latent Semantic Analysis workflows original ↗

30 Dec 2019

3D ornaments (by texturized polygons)original ↗

24 Dec 2019

Wolfram Live-Coding Series on Quantile Regression workflows original ↗

18 Sept 2019

A monad for Latent Semantic Analysis workflows original ↗

13 Sept 2019

Finding all structural breaks in time series original ↗

31 Jul 2019

Call graph generation for context functions original ↗

1 Jan 2019

2018

Parametrized event records data transformations original ↗

5 Oct 2018

QRMon for some credit risk article original ↗

24 Aug 2018

A monad for Quantile Regression workflows original ↗

1 Aug 2018

Mathematica-vs-R: Deep learning examples original ↗

15 Jun 2018

A monad for classification workflows original ↗

15 May 2018

Progressive Machine Learning Examples original ↗

13 Apr 2018

Applying Artificial Intelligence and Machine Learning to Finance and Technology original ↗

21 Feb 2018

2017

The Great conversation in USA presidential speeches original ↗

24 Dec 2017

Phone dialing conversational agent original ↗

12 Jul 2017

Monad code generation and extension original ↗

23 Jun 2017

Comparison of dimension reduction algorithms over mandala images generation original ↗

10 Feb 2017

Tries with frequencies in Java original ↗

31 Jan 2017

2016

Text analysis of Trump tweets original ↗

21 Nov 2016

Pareto principle adherence examples original ↗

17 Nov 2016

Handwritten digits recognition by matrix factorization original ↗

12 Nov 2016

Mathematica vs. R at GitHub original ↗

12 Nov 2016

ROC for classifier ensembles, bootstrapping, damaging, and interpolation original ↗

15 Oct 2016

Basic example of using ROC with Linear regression original ↗

12 Oct 2016

Contingency tables creation examples original ↗

4 Oct 2016

Adaptive numerical Lebesgue integration by set measure estimates original ↗

1 Jul 2016

Making Chernoff faces for data visualization original ↗

3 Jun 2016

Comparison of PCA, NNMF, and ICA over image de-noising original ↗

26 May 2016

Independent component analysis for multidimensional signals original ↗

23 May 2016

Comparison of PCA and NNMF over image de-noising original ↗

7 May 2016

Finding outliers in 2D and 3D numerical data original ↗

30 Apr 2016

Creating and programming domain specific languages original ↗

22 Mar 2016

Object-Oriented Design Patterns in Mathematica original ↗

20 Mar 2016

UML diagrams creation and generation original ↗

14 Mar 2016

Importance of variables investigation original ↗

11 Jan 2016

2015

RSparseMatrix for sparse matrices with named rows and columns original ↗

8 Oct 2015

Finding local extrema in noisy data using Quantile Regression original ↗

27 Sept 2015

2014

Simple time series conversational engine original ↗

29 Nov 2014

Directional quantile envelopes in 3D original ↗

16 Nov 2014

Directional quantile envelopes original ↗

3 Nov 2014

Removal of sub-trees in tries original ↗

12 Oct 2014

Find Fit for Non-linear data original ↗

19 Apr 2014

Classification and association rules for census income data original ↗

30 Mar 2014

Enhancements of MosaicPlot original ↗

24 Mar 2014

Mosaic plots for data visualization original ↗

17 Mar 2014

Natural language processing with functional parsers original ↗

13 Feb 2014

Classification of genome data with n-gram models original ↗

27 Jan 2014

Markov chains n-gram model implementation original ↗

25 Jan 2014

Estimation of conditional density distributions original ↗

13 Jan 2014

OPL code for quantile regression with B-splines original ↗

10 Jan 2014

Quantile regression with B-splines original ↗

1 Jan 2014

2013

Quantile regression robustness original ↗

23 Dec 2013

Quantile regression through linear programming original ↗

16 Dec 2013