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Day: February 23, 2023

Calculating Log Likelihood Ratios with jeva

Peter M.B. Cahusac takes us through a jamovi package:

Ever wanted to try doing an evidential analysis? You may have found it difficult to find a statistical platform to do it. Now there is the jamovi module jeva which can provide log likelihood ratios for a range of common statistical tests.

Imagine for a moment that we wish to carry out a statistical test on our sample of data. We do not want to know whether the procedure we routinely use gives us the correct answer with a specified error rate (such as the Type I error) – the frequentist approach. Nor do we want to concern ourselves with possible a priori probabilities of hypotheses being true – the Bayesian approach. We need to know whether a statistic from this particular set of data is consistent with one or more hypothetical values. Also, let’s say that we weren’t happy with how much data we had collected (a familiar problem?), and just added more when convenient. Welcome to the likelihood (or evidential) approach!

Read on for an explanation and how to try jeva out.

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Calibrating and Plotting a Time Series with healthyR.ts

Steven Sanderson builds a plot:

In time series analysis, it is common to split the data into training and testing sets to evaluate the accuracy of a model. However, it is important to ensure that the model is calibrated on the training set before evaluating its performance on the testing set. The {healthyR.ts} library provides a function called calibrate_and_plot() that simplifies this process.

Click through for the function’s input parameters and an example of how to use it.

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Tips for Kafka Streams Developers

Ludovic Dehon shares some advice:

We built Kestra, an open-source data orchestration and scheduling platform, and we decided to use Kafka as the central datastore to build a scalable architecture. We rely heavily on Kafka Streams for most of our services (the executor and the scheduler) and have made some assumptions on how it handles the workload.

However, Kafka has some restrictions since it is not a database, so we need to deal with the constraints and adapt the code to make it work with Kafka. We will cover topics, such as using the same Kafka topic for source and destination, and creating a custom joiner for Kafka Streams, to ensure high throughput and low latency while adapting to the constraints of Kafka and making it work with Kestra.

Click through for several tips.

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Aggregations and Distinct Counts in Power BI

Phil Seamark doesn’t have time to wait for Power BI to count:

This article aims to show how you can speed up distinct count calculations in Power BI using the built-in user-defined aggregations feature. The user-defined aggregation feature in Power BI is designed to work with direct query models and usually gets used for calculations such as SUM, MIN, MAX etc. However, it can also work well for distinct count calculations using the pattern shown in this article.

It’s an interesting partial aggregation approach which works really well when the distinct count is a small percentage of the total.

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Synapse and Azure ML Pipelines

Santosh Thomas integrates two Azure products:

As more customers standardize on the Synapse data platform, enabling machine learning workflows through Azure Machine Learning (Azure ML) becomes particularly interesting. This is especially true as more customers look to bring their data engineering and data science practices together and mature capabilities on both sides.

The goal of this blog post is to highlight how Synapse and Azure ML can work well together to deliver key insights. This is motivated by a scenario where a customer modernized their data platform on Azure Synapse but was looking to improve their data science practices through Azure ML. The focus of this blog is to expose existing functionality, and it is not a “hardened” solution with security or other cloud best practice implementations. The workflow steps also assume some level of comfort with Python and working with the Azure Python SDKs.

There was a time in which Microsoft wanted us to remain in Synapse for machine learning tasks, but that time is gone: the emphasis is definitely to do machine learning tasks in Azure ML, regardless of where the data lives…unless there’s a Spark job involved, in which case things get all weird again.

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