#bayesian analysis

toddlers themselves? 3/10 I’ve mediated too many arguments to give them any higher 😞 love them tho

This paper proposes an algorithm, named HWK-Sets, based on K-Means, suited for clustering data which are variable-sized sets of elementary items. An example of such data occurs in the analysis of medical diagnosis, where the goal is to detect human subjects who share common diseases so as to predict future illnesses from previous medical history possibly. Clustering sets is difficult because data objects do not have numerical attributes and therefore it is not possible to use the classical Euclidean distance upon which K-Means is normally based. An adaptation of the Jaccard distance between sets is used, which exploits application-sensitive information. More in particular, the Hartigan and Wong variation of K-Means is adopted, which can favor the fast attainment of a careful solution. The HWK-Sets algorithm can flexibly use various stochastic seeding techniques. Since the difficulty of calculating a mean among the sets of a cluster, the concept of a medoid is employed as a cluster representative (centroid), which always remains a data object of the application. The paper describes the HWK-Sets clustering algorithm and outlines its current implementation in Java based on parallel streams. After that, the efficiency and accuracy of the proposed algorithm are demonstrated by applying it to 15 benchmark datasets.

Read More About This Article: https://crimsonpublishers.com/oabb/fulltext/OABB.000564.php

IT’S A BAYESIAN ANALYSIS OF LUCY WESTENRA’S LIKELY BLOOD TYPE BASED ON BLOOD TYPE PREVALENCE IN EUROPE AND THE RESULTS OF HER TRANSFUSIONS

I WANT TO SEND THIS BACK IN TIME TO MY 14-YEAR-OLD SELF WHO HAD JUST LEARNED ABOUT BLOOD TYPES AND WAS READING DRACULA FOR THE FIRST TIME

Worldwide Bayesian Causal Impact Analysis of Vaccine Administration on Deaths and Cases Associated with COVID-19: A BigData Analysis of 145 Countries

Abstract

Policy makers and mainstream news anchors have promised the public that the COVID-19 vaccine rollout worldwide would reduce symptoms, and thereby cases and deaths associated with COVID-19. While this vaccine rollout is still in progress, there is a large amount of public data available that permits an analysis of the effect of the vaccine rollout on COVID-19 related cases and deaths. Has this public policy treatment produced the desired effect?

One manner to respond to this question can begin by implementing a Bayesian causal analysis comparing both pre- and post-treatment periods. This study analyzed publicly available COVID-19 data from OWID (Hannah Ritchie and Roser 2020Hannah Ritchie, Lucas Rodés-Guirao, Edouard Mathieu, and Max Roser. 2020. “Coronavirus Pandemic (COVID-19).” Our World in Data.) utlizing the R package CausalImpact(Brodersen et al. 2015Brodersen, Kay H., Fabian Gallusser, Jim Koehler, Nicolas Remy, and Steven L. Scott. 2015. “Inferring Causal Impact Using Bayesian Structural Time-Series Models.”Annals of Applied Statistics 9: 247–74. https://doi.org/10.1214/14-aoas788.) to determine the causal effect of the administration of vaccines on two dependent variables that have been measured cumulatively throughout the pandemic: total deaths per million (y1y1) and total cases per million (y2y2). After eliminating all results from countries with p > 0.05, there were 128 countries for y1y1 and 103 countries for y2y2 to analyze in this fashion, comprising 145 unique countries in total (avg. p < 0.004).

Results indicate that the treatment (vaccine administration) has a strong and statistically significant propensity to causally increase the values in either y1y1 or y2y2 over and above what would have been expected with no treatment. y1y1 showed an increase/decrease ratio of (+115/-13), which means 89.84% of statistically significant countries showed an increase in total deaths per million associated with COVID-19 due directly to the causal impact of treatment initiation. y2y2 showed an increase/decrease ratio of (+105/-16) which means 86.78% of statistically significant countries showed an increase in total cases per million of COVID-19 due directly to the causal impact of treatment initiation. Causal impacts of the treatment on y1y1 ranges from -19% to +19015% with an average causal impact of +463.13%. Causal impacts of the treatment on y2y2 ranges from -46% to +12240% with an average causal impact of +260.88%. Hypothesis 1 Null can be rejected for a large majority of countries.

This study subsequently performed correlational analyses on the causal impact results, whose effect variables can be represented as y1.Ey1.E and y2.Ey2.Erespectively, with the independent numeric variables of: days elapsed since vaccine rollout began (n1n1), total vaccination doses per hundred (n2n2), total vaccine brands/types in use (n3n3) and the independent categorical variables continent (c1c1), country (c2c2), vaccine variety (c3c3). All categorical variables showed statistically significant (avg. p: < 0.001) postive Wilcoxon signed rank values (y1.Ey1.E VV:[c1c1 3.04; c2c2: 8.35; c3c3: 7.22] and y2.Ey2.E VV:[c1c1 3.04; c2c2: 8.33; c3c3: 7.19]). This demonstrates that the distribution of y1.Ey1.E and y2.Ey2.E was non-uniform among categories. The Spearman correlation between n2n2 and y2.Ey2.E was the only numerical variable that showed statistically significant results (y2.Ey2.E ~ n2n2: ρρ: 0.34 CI95%[0.14, 0.51], p: 4.91e-04). This low positive correlation signifies that countries with higher vaccination rates do not have lower values for y2.Ey2.E, slightly the opposite in fact. Still, the specifics of the reasons behind these differences between countries, continents, and vaccine types is inconclusive and should be studied further as more data become available. Hypothesis 2 Null can be rejected for c1c1, c2c2, c3c3 and n2n2 and cannot be rejected for n1n1, and n3n3.

The statistically significant and overwhelmingly positive causal impact after vaccine deployment on the dependent variables total deaths and total cases per million should be highly worrisome for policy makers. They indicate a marked increase in both COVID-19 related cases and death due directly to a vaccine deployment that was originally sold to the public as the “key to gain back our freedoms.” The effect of vaccines on total cases per million and its low positive association with total vaccinations per hundredsignifies a limited impact of vaccines on lowering COVID-19 associated cases. These results should encourage local policy makers to make policy decisions based on data, not narrative, and based on local conditions, not global or national mandates. These results should also encourage policy makers to begin looking for other avenues out of the pandemic aside from mass vaccination campaigns.

Some variables that could be included in future analyses might include vaccine lot by country, the degree of prevalence of previous antibodies against SARS-CoV or SARS-CoV-2 in the population before vaccine administration begins, and the Causal Impact of ivermectin on the same variables used in this study.

“Untruth naturally afflicts historical information. There are various reasons that make this unavoidable. One of them is partisanship for opinions and schools. If the soul is impartial in receiving information, it devotes to that information the share of critical investigation the information deserves, and its truth or untruth thus becomes clear. However, if the soul is infected with partisanship for a particular opinion or sect, it accepts without a moment’s hesitation the information that is agreeable to it. Prejudice and partisanship obscure the critical faculty and preclude critical investigation. The result is that falsehoods are accepted and transmitted” (Muhammad ibn Khaldun al-Hadrami 1379, 1–2Muhammad ibn Khaldun al-Hadrami, Abu Zayd ’Abd ar-Rahman ibn. 1379. Muqaddimat Ibn Khaldun Li-Kitab Al-’ibar Wa-Diwan Al-Mubtada’ Wa-Al-Khabar Fi Ayyam Al-’Arab Wa-Al-’Ajam Wa-Al-Barbar, Wa-Man ’asarahum Min Dhawi Al-Sultan Al-Akbar. Misr : al-Mataba’ah al-Bahiyah al-Misriyah. http://archive.org/details/McGillLibrary-rbsc_isl_kitab-al-ibar_muqaddimah_d167l2361900z-16098.).

Bayesian Analysis

Maruti Suzuki India

By:Monica Ribeiro

 1.  Promotional Event

At Maruti Suzuki India the dealership we are thinking of studding a marketing analysis on a promotional event which will indicate how many males and females are satisfied with our customer satisfaction rate. For that reason, we will study which customer type are buying by gender, and who wins men or woman in satisfaction of the buying process. Why we are doing this is because that way we can satisfy customer needs by having the right vehicle is stock.

 2.Target Customers 

My target pool is toward the gender female or male and how happy are they with buying a vehicle in this case who wins are the male by potential customer. We plan to contact them directly and mail them offers to bring them back and finalize a sale. The cost can vary between $2000 to $4000 dollars. The cost will be justified when we sale them the vehicle.

3. Selected Sampling

 A)    The probability that the customers is a woman or male

P=60/124=0.483

B)    The probably that the potential customer is a woman

P=23/124=0.185

4. Lottery Analysis 

A)    Loyal Customers

P=15/124=0.129

B)    Potential Customers

P=58/124=0.467

C)    New Consumers

P=30/124=0.241

D)    Dissatisfied customer

P=15/124=0.120

In this analysis the Potential Customers have a better chance to win with a .467%.

 5. Bayesian Analysis 

 Bayesian inference is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. Bayesian inference is an important technique in statistics, and especially in mathematical statistics. Bayesian updating is particularly important in the dynamic analysis of a sequence of data. Bayesian inference has found application in a wide range of activities, including science, engineering, philosophy, medicine, sport, and law. In the philosophy of decision theory, Bayesian inference is closely related to subjective probability, often called "Bayesian probability.

6.  Expansion Recommendation

Based on our analyses in Maruti Suzuki India the data above and given the test data, in our dealership, we should expand to India. Our focus should be on the client satisfaction and needs. Especially the ones in rural areas. We do have potential here in India since is a strong market. We must do what’s necessary to bring in more customers and have them also communicate to friends and family their success story here.

 References:

“Bayesian Analysis of Racial Bias in Police Shootings in the U.S.” https://t.co/GbEpju45cn (map from “Mapping Police Violence”, cited in paper)

A Multi-Level Bayesian Analysis of Racial Bias in Police Shootings at the County-Level in the United States, 2011–2014

Abstract

A geographically-resolved, multi-level Bayesian model is used to analyze the data presented in the U.S. Police-Shooting Database (USPSD) in order to investigate the extent of racial bias in the shooting of American civilians by police officers in recent years. In contrast to previous work that relied on the FBI’s Supplemental Homicide Reports that were constructed from self-reported cases of police-involved homicide, this data set is less likely to be biased by police reporting practices. County-specific relative risk outcomes of being shot by police are estimated as a function of the interaction of: 1) whether suspects/civilians were armed or unarmed, and 2) the race/ethnicity of the suspects/civilians. The results provide evidence of a significant bias in the killing of unarmed black Americans relative to unarmed white Americans, in that the probability of being {black, unarmed, and shot by police} is about 3.49 times the probability of being {white, unarmed, and shot by police} on average. Furthermore, the results of multi-level modeling show that there exists significant heterogeneity across counties in the extent of racial bias in police shootings, with some counties showing relative risk ratios of 20 to 1 or more. Finally, analysis of police shooting data as a function of county-level predictors suggests that racial bias in police shootings is most likely to emerge in police departments in larger metropolitan counties with low median incomes and a sizable portion of black residents, especially when there is high financial inequality in that county. There is no relationship between county-level racial bias in police shootings and crime rates (even race-specific crime rates), meaning that the racial bias observed in police shootings in this data set is not explainable as a response to local-level crime rates.

Cody T. Ross   

Published: November 5, 2015

In a recent article by Debbie Madden, CEO and co-founder of Stride, she argues that a budget is often better than an estimate.

Her argument is that getting a good estimate of a project often takes a lot of time and cost. And in the end, a lot of software projects run “X”% over the estimate. So she advocates for a budget.

Because software is always a creative process, her “budget” is not what we…