Data Science #2 - "Application of the Logistic Function to Bio-Assays" (1944), Berkson Joseph

Data Science #2 - "Application of the Logistic Function to Bio-Assays" (1944), Berkson Joseph

"Application of the Logistic Function to Bio-Assays" (1944), Berkson Joseph

It gained further prominence in the 20th century through applications in various fields, including biology and bio-assay. Joseph Berkson's 1944 paper, 'Application of the Logistic Function to Bio-Assay,' was pivotal in popularizing its use for estimating drug potency.


Berkson argued that the logistic function was a more statistically manageable and theoretically sound alternative to the probit function, which assumed that individual susceptibilities to a drug follow a normal distribution.

The logistic function's ability to be easily linearized via the logit transformation simplifies parameter estimation, making it an attractive choice for analyzing dose-response data.

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Jaksot(33)

 Data Science #34 - The deep learning original paper review, Hinton, Rumelhard & Williams (1985)

Data Science #34 - The deep learning original paper review, Hinton, Rumelhard & Williams (1985)

On the 34th episode, we review the 1986 paper, "Learning representations by back-propagating errors" , which was pivotal because it provided a clear, generalized framework for training neural networks...

23 Marras 202546min

Data Science #33 - The Backpropagation method, Paul Werbos (1980)

Data Science #33 - The Backpropagation method, Paul Werbos (1980)

On the 33rd episdoe we review Paul Werbos’s “Applications of Advances in Nonlinear Sensitivity Analysis” which presents efficient methods for computing derivatives in nonlinear systems, drastically re...

3 Marras 202557min

 Data Science #32 - A Markovian Decision Process, Richard Bellman (1957)

Data Science #32 - A Markovian Decision Process, Richard Bellman (1957)

We reviewed Richard Bellman’s “A Markovian Decision Process” (1957), which introduced a mathematical framework for sequential decision-making under uncertainty. By connecting recurrence relations to M...

19 Syys 202546min

 Data Science #31 - Correlation and causation (1921), Wright Sewall

Data Science #31 - Correlation and causation (1921), Wright Sewall

On the 31st episode of the podcast, we add Liron to the team, we review a gem from 1921, where Sewall Wright introduced path analysis, mapping hypothesized causal arrows into simple diagrams and provi...

26 Heinä 202548min

Data Science #30 - The Bootstrap Method (1977)

Data Science #30 - The Bootstrap Method (1977)

In the 30th episode we review the the bootstrap, method which was introduced by Bradley Efron in 1979, is a non-parametric resampling technique that approximates a statistic’s sampling distribution by...

30 Touko 202541min

Data Science #29 - The Chi-square automatic interaction detection(CHAID) algorithm (1979)

Data Science #29 - The Chi-square automatic interaction detection(CHAID) algorithm (1979)

In the 29th episode, we go over the 1979 paper by Gordon Vivian Kass that introduced the CHAID algorithm.CHAID (Chi-squared Automatic Interaction Detection) is a tree-based partitioning method introdu...

23 Touko 202541min

Data Science #28 - The Bloom filter algorithm

Data Science #28 - The Bloom filter algorithm

In the 28th episode, we go over Burton Bloom's Bloom filter from 1970, a groundbreaking data structure that enables fast, space-efficient set membership checks by allowing a small, controllable rate o...

23 Touko 202539min

Data Science #27 - The History of Least Squares (1877)

Data Science #27 - The History of Least Squares (1877)

Mansfield Merriman's 1877 paper traces the historical development of the Method of Least Squares, crediting Legendre (1805) for introducing the method, Adrain (1808) for the first formal probabilistic...

2 Huhti 202532min

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