# Posts by Tags

## Hypothesis Tests Part 1: Bayesian Inference

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Every quantity that is estimated from data, such as the mean or the variance, is subject to uncertainties of the measurements due to data collection. If a different sample of measurements is collected, value fluctuations will certainly give rise to a different set of measurements, even if the experiments are performed under the same conditions. The use of different data samples to measure the same value results in a sampling distribution that characterize the quantity in consideration. This distribution is used to characterize the “true” value of the quantity in consideration. This blog post is dedicated to present how the collected data is employed to test hypotheses of the quantity being measured.

## Using Bayesian Modelling to Predict the Number of COVID19 in Brazil

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Abstract. In this notebook, I use Bayesian modelling to predict the rate of growth of confirmed cases of COVID19 in Brasil.

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# Some section

## My Quick Reference Guide For A Few Natural Language Processing Techniques

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Natural language processing (NLP) is a field of study dedicated to analyze of natural languages. In particular, using statistics and algorithms.

## My Quick Reference Guide For A Few Natural Language Processing Techniques

Published:

Natural language processing (NLP) is a field of study dedicated to analyze of natural languages. In particular, using statistics and algorithms.

## Querying the Latest Record

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In this gist, I show how to get the latest record or a user based on a datetime column.

## Hypothesis Tests Part 2: Statistical Inference

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In this post, I present an overview of statistical tests. The goal of calculating a test statistic is to decided if the null hypothesis is true. Once value of the test-statistic is obtained, it is compared with a pre-defined critical value. If the test statistic is found to be greater than the critical value, then hypothesis is rejected.

## Hypothesis Tests Part 1: Bayesian Inference

Published:

Every quantity that is estimated from data, such as the mean or the variance, is subject to uncertainties of the measurements due to data collection. If a different sample of measurements is collected, value fluctuations will certainly give rise to a different set of measurements, even if the experiments are performed under the same conditions. The use of different data samples to measure the same value results in a sampling distribution that characterize the quantity in consideration. This distribution is used to characterize the “true” value of the quantity in consideration. This blog post is dedicated to present how the collected data is employed to test hypotheses of the quantity being measured.

## Cumulative Sum with Pandas

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In this gist, I calculate the cumulative sum of the column no, based on the columns nameand day.

## Datetime Resample

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In this gist, I calculate aggregate the datetime column according to different periods (e.g. day, week, and month)

## Signing git commits with gpg

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I found this post on how to sign commits with gpg on Medium, and I copied to my blog so I can keep for my records. Please, visit the original source at:

## Signing git commits with gpg

Published:

I found this post on how to sign commits with gpg on Medium, and I copied to my blog so I can keep for my records. Please, visit the original source at:

## Signing git commits with gpg

Published:

I found this post on how to sign commits with gpg on Medium, and I copied to my blog so I can keep for my records. Please, visit the original source at:

Published:

This gist contains my default settings for a Jupyter notebook as a header.

## Hypothesis Tests Part 2: Statistical Inference

Published:

In this post, I present an overview of statistical tests. The goal of calculating a test statistic is to decided if the null hypothesis is true. Once value of the test-statistic is obtained, it is compared with a pre-defined critical value. If the test statistic is found to be greater than the critical value, then hypothesis is rejected.

## Hypothesis Tests Part 1: Bayesian Inference

Published:

Every quantity that is estimated from data, such as the mean or the variance, is subject to uncertainties of the measurements due to data collection. If a different sample of measurements is collected, value fluctuations will certainly give rise to a different set of measurements, even if the experiments are performed under the same conditions. The use of different data samples to measure the same value results in a sampling distribution that characterize the quantity in consideration. This distribution is used to characterize the “true” value of the quantity in consideration. This blog post is dedicated to present how the collected data is employed to test hypotheses of the quantity being measured.

## Pandas Value Counts

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The value_counts() function in the popular python data science library Pandas is a quick way to count the unique values in a single column otherwise known as a series of data.

## Querying the Latest Record

Published:

In this gist, I show how to get the latest record or a user based on a datetime column.

## jupyter

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This gist contains my default settings for a Jupyter notebook as a header.

## Plotting a Four-Dimensional Heatmap

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Here is show how I developed a four-dimensional heatmap.

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# I moved my blog from Wordpress

## Find Row Closest to a Value

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In this gist, I find what is the closest row to a given value.

## Pandas Value Counts

Published:

The value_counts() function in the popular python data science library Pandas is a quick way to count the unique values in a single column otherwise known as a series of data.

## Find Row Closest to a Value

Published:

In this gist, I find what is the closest row to a given value.

## Datetime Resample

Published:

In this gist, I calculate aggregate the datetime column according to different periods (e.g. day, week, and month)

## Cumulative Sum with Pandas

Published:

In this gist, I calculate the cumulative sum of the column no, based on the columns nameand day.

## Plotting a Four-Dimensional Heatmap

Published:

Here is show how I developed a four-dimensional heatmap.

## Pandas Value Counts

Published:

The value_counts() function in the popular python data science library Pandas is a quick way to count the unique values in a single column otherwise known as a series of data.

## Find Row Closest to a Value

Published:

In this gist, I find what is the closest row to a given value.

Published:

This gist contains my default settings for a Jupyter notebook as a header.

## Datetime Resample

Published:

In this gist, I calculate aggregate the datetime column according to different periods (e.g. day, week, and month)

## Cumulative Sum with Pandas

Published:

In this gist, I calculate the cumulative sum of the column no, based on the columns nameand day.

## Datetime Resample

Published:

In this gist, I calculate aggregate the datetime column according to different periods (e.g. day, week, and month)

## Hypothesis Tests Part 2: Statistical Inference

Published:

In this post, I present an overview of statistical tests. The goal of calculating a test statistic is to decided if the null hypothesis is true. Once value of the test-statistic is obtained, it is compared with a pre-defined critical value. If the test statistic is found to be greater than the critical value, then hypothesis is rejected.

## Hypothesis Tests Part 2: Statistical Inference

Published:

In this post, I present an overview of statistical tests. The goal of calculating a test statistic is to decided if the null hypothesis is true. Once value of the test-statistic is obtained, it is compared with a pre-defined critical value. If the test statistic is found to be greater than the critical value, then hypothesis is rejected.

## Hypothesis Tests Part 1: Bayesian Inference

Published:

Every quantity that is estimated from data, such as the mean or the variance, is subject to uncertainties of the measurements due to data collection. If a different sample of measurements is collected, value fluctuations will certainly give rise to a different set of measurements, even if the experiments are performed under the same conditions. The use of different data samples to measure the same value results in a sampling distribution that characterize the quantity in consideration. This distribution is used to characterize the “true” value of the quantity in consideration. This blog post is dedicated to present how the collected data is employed to test hypotheses of the quantity being measured.