(1) Bayes' theorem provides a framework for updating probabilities based on new information or evidence.
(2) It allows calculating the conditional probability of a hypothesis being true given observed data by combining the prior probability of the hypothesis with the likelihood of making the observation under that hypothesis.
(3) Bayesian inference uses Bayes' theorem to update beliefs as new data becomes available, representing uncertainty about unknown parameters as a probability distribution that is continually refined as more evidence is accumulated.
Basic statistics for algorithmic tradingQuantInsti
In this presentation we try to understand the core basics of statistics and its application in algorithmic trading.
We start by defining what statistics is. Collecting data is the root of statistics. We need data to analyse and take quantitative decisions.
While analyzing, there are certain parameters for statistics, this branches statistics into two - descriptive statistics & inferential statistics.
This data that we have collected can be classified into uni-variate and bi-variate. It also tries to explain the fundamental difference.
Going Further we also cover topics like regression line, Coefficient of Determination, Homoscedasticity and Heteroscedasticity.
In this way the presentation look at various aspects of statistics which are used for algorithmic trading.
To learn the advanced applications of statistics for HFT & Quantitative Trading connect with us one our website: www.quantinsti.com.
If we measure a random variable many times, we can build up a distribution of the values it can take.
Imagine an underlying distribution of values which we would get if it was possible to take more and more measurements under the same conditions.
This gives the probability distribution for the variable.
Make use of the PPT to have a better understanding of Probability Distribution.
In this slide, variables types, probability theory behind the algorithms and its uses including distribution is explained. Also theorems like bayes theorem is also explained.
Basic statistics for algorithmic tradingQuantInsti
In this presentation we try to understand the core basics of statistics and its application in algorithmic trading.
We start by defining what statistics is. Collecting data is the root of statistics. We need data to analyse and take quantitative decisions.
While analyzing, there are certain parameters for statistics, this branches statistics into two - descriptive statistics & inferential statistics.
This data that we have collected can be classified into uni-variate and bi-variate. It also tries to explain the fundamental difference.
Going Further we also cover topics like regression line, Coefficient of Determination, Homoscedasticity and Heteroscedasticity.
In this way the presentation look at various aspects of statistics which are used for algorithmic trading.
To learn the advanced applications of statistics for HFT & Quantitative Trading connect with us one our website: www.quantinsti.com.
If we measure a random variable many times, we can build up a distribution of the values it can take.
Imagine an underlying distribution of values which we would get if it was possible to take more and more measurements under the same conditions.
This gives the probability distribution for the variable.
Make use of the PPT to have a better understanding of Probability Distribution.
In this slide, variables types, probability theory behind the algorithms and its uses including distribution is explained. Also theorems like bayes theorem is also explained.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
for beginners, providing thorough training in areas such as SEO, digital communication marketing, and PPC training in Noida. After finishing the program, students receive the certifications recognised by top different universitie, setting a strong foundation for a successful career in digital marketing.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
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Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
2. Outline
• Probability distributions
• Joint probability
• Marginal probability
• Conditional probability
• Bayes’ theorem
• Bayesian inference
• Coin toss example
3. “Probability is orderly opinion and
inference from data is nothing other than
the revision of such opinion in the light
of relevant new information.”
Eliezer S. Yudkowsky
16. Example 1
P(A) = probability of liver disease = 0.10
P(B) = probability of alcoholism = 0.05
P(B|A) = 0.07
P(A|B) = ?
𝑃 𝐴 𝐵 =
𝑃 𝐵 𝐴 ×𝑃 𝐴
𝑃 𝐵
=
0.07 × 0.10
0.05
= 0.14
In other words, if the patient is an alcoholic, their chances of having liver disease is 0.14 (14%)
10% of patients in a clinic have liver disease. Five percent of the clinic’s patients are alcoholics.
Amongst those patients diagnosed with liver disease, 7% are alcoholics. You are interested in knowing
the probability of a patient having liver disease, given that he is an alcoholic.
17. Example 2
A disease occurs in 0.5% of the population
A diagnostic test gives a positive result in:
◦ 99% of people with the disease
◦ 5% of people without the disease (false positive)
A person receives a positive result
What is the probability of them having the disease, given a positive result?
19. 𝑃 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑡𝑒𝑠𝑡 = 𝑃 𝐷 𝑃𝑇 × 𝑃 𝐷 + 𝑃 𝑃𝑇 ~𝐷 × 𝑃 ~𝐷
= 0.99 × 0.005 + 0.05 × 0.995 = 0.005
Where:
𝑃 𝐷 = chance of having the disease
𝑃 ~𝐷 = chance of not having the disease
Remember: 𝑃 ~𝐷 = 1 − 𝑃 𝐷
𝑃 𝑃𝑇 𝐷 = chance of positive test given that disease is present
𝑃 𝑃𝑇 ~𝐷 = chance of positive test given that the disease isn’t present
22. Frequentist models in practice
• Model: 𝑌 = 𝑋𝜃 + 𝜀
• Data X is random variable, while parameters 𝜽 are unknown but fixed
• We assume there is a true set of parameters, or true model of the world, and we
are concerned with getting the best possible estimate
• We are interested in point estimates of parameters given the data
23. Bayesian models in practice
• Model: 𝑌 = 𝑋𝜃 + 𝜀
• Data X is fixed, while parameters 𝜃 are considered to be random
variables
• There is no single set of parameters that denotes a true model of the
world - we have parameters that are more or less probable
• We are interested in distribution of parameters given the data
24. Bayesian Inference
• Provides a dynamic model through which our belief is constantly updated as
we add more data
• Ultimate goal is to calculate the posterior probability density, which is
proportional to the likelihood (of our data being correct) and our prior
knowledge
• Can be used as model for the brain (Bayesian brain), history and human
behaviour
25. Bayes rule
Likelihood
• How good are our parameters given the data
• Prior knowledge is incorporated and used to update our beliefs about the
parameters
𝑃 𝜃 𝐷 =
𝑃 𝐷 𝜃 × 𝑃 𝜃
𝑃 𝐷
∝ 𝑃 𝐷 𝜃 × 𝑃 𝜃
Prior
Posterior
Evidence 𝑃 𝐷 𝜃 × 𝑃 𝜃 𝑑𝜃
26. Generative models
• Specify a joint probability distribution over all variables (observations and
parameters) requires a likelihood function and a prior:
𝑃 𝐷, 𝜃 𝑚 = 𝑃 𝐷 𝜃, 𝑚 × 𝑃 𝜃 𝑚 ∝ 𝑃 𝜃 𝐷, 𝑚
• Model comparison based on the model evidence:
𝑃 𝐷 𝑚 = 𝑃 𝐷 𝜃, 𝑚 × 𝑃 𝜃 𝑚 𝑑𝜃
27. Principles of Bayesian Inference
• Formulation of a generative model
• Observation of data
• Model inversion – updating one’s belief
Model
Measurement
𝑃 𝜃 𝐷 ∝ 𝑃 𝐷 𝜃 × 𝑃(𝜃)
data D
Likelihood function 𝑃 𝐷 𝜃
Prior distribution 𝑃(𝜃)
Posterior distribution
Model evidence
28. Priors
Priors can be of different sorts, e.g.
• empirical (previous data)
• uninformed
• principled (e.g. positivity constraints)
• shrinkage
Conjugate priors = posterior 𝑃 𝐷 𝜃 is in the same family as the prior 𝑃 𝜃
29. • effect of more
informative prior
distributions on
the posterior
distribution
𝑃 𝜃 𝐷 ∝ 𝑃 𝐷 𝜃 × 𝑃 𝜃
∝ 𝑙𝑖𝑘𝑒𝑙𝑖ℎ𝑜𝑜𝑑 × 𝑝𝑟𝑖𝑜𝑟
30. 𝑃 𝜃 𝐷 ∝ 𝑃 𝐷 𝜃 × 𝑃 𝜃
∝ 𝑙𝑖𝑘𝑒𝑙𝑖ℎ𝑜𝑜𝑑 × 𝑝𝑟𝑖𝑜𝑟
• effect of larger
sample sizes on
the posterior
distribution
31. Example: Coin flipping model
• Someone flips a coin
• We don’t know if the coin is fair or not
• We are told only the outcome of the coin flipping
32. • 1st Hypothesis: Coin is fair, 50% Heads or Tails
• 2nd Hypothesis: Both sides of the coin are heads, 100% Heads
Example: Coin flipping model
33. Example: Coin flipping model
• 1st Hypothesis: Coin is fair, 50% Heads or Tails
𝑃 𝐴 = 𝑓𝑎𝑖𝑟 𝑐𝑜𝑖𝑛 = 0.99
• 2nd Hypothesis: Both sides of the coin are heads, 100% Heads
𝑃 𝐴 = 𝑢𝑛𝑓𝑎𝑖𝑟 𝑐𝑜𝑖𝑛 = 0.01
39. Example: Coin flipping model
𝐷 = 𝑇 𝐻 𝑇 𝐻 𝑇 𝑇 𝑇 𝑇 𝑇 𝑇 and we think a priori that the coin is fair:
𝑃 𝑓𝑎𝑖𝑟 = 0.8, 𝑃 𝑏𝑒𝑛𝑡 = 0.2
Evidence for a fair model is:
𝑃 𝐷 𝑓𝑎𝑖𝑟 = 0.510 ≈ 0.001
And for a bent model:
𝑃 𝐷 𝑏𝑒𝑛𝑡 = 𝑃 𝑏𝑒𝑛𝑡 𝜃, 𝐷 × 𝑃 𝜃 𝑏𝑒𝑛𝑡 𝑑𝜃
= 𝜃2 × (1 − 𝜃)8𝑑𝜃 = 𝐵(3,9) ≈ 0.002
Posterior for the models:
𝑃 𝑓𝑎𝑖𝑟 𝐷 ∝ 0.001 × 0.8 = 0.0008
𝑃 𝑏𝑒𝑛𝑡 𝐷 ∝ 0.002 × 0.2 = 0.0004
40. "A Bayesian is one who,
vaguely expecting a horse,
and catching a glimpse of a donkey,
strongly believes he has seen a mule."
41. References
• Previous MfD slides
• Bayesian statistics (a very brief introduction) – Ken Rice
• http://www.statisticshowto.com/bayes-theorem-problems/
• Slides “Bayesian inference and generative models” of K.E. Stephan
• Introslides to probabilistic & unsupervised learning of M. Sahani
• Animations: https://blog.stata.com/2016/11/01/introduction-to-
bayesian-statistics-part-1-the-basic-concepts/