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Uncertainty - Coggle Diagram
Uncertainty
Uncertainty
Artificial intelligence (AI) uncertainty is when there’s not enough information or ambiguity in data or decision-making. It is a fundamental concept in AI, as real-world data is often noisy and incomplete. AI systems must account for uncertainty to make informed decisions.
Data Uncertainty:
AI models are trained on data, and the quality and accuracy of the data can affect the performance of the model.
Model Uncertainty:
AI models are complex and can have various parameters and hyperparameters that need to be tuned.
Algorithmic Uncertainty:
AI algorithms can be based on different mathematical formulations, leading to different results for the same problem.
Environmental Uncertainty:
AI systems operate in dynamic environments, and changes in the environment can affect the performance of the system.
Human Uncertainty:
AI systems often interact with humans, either as users or as part of the decision-making process.
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Andrey Markov
A A Markov was a Russian mathematician who is is best known for his work in probability and for stochastic processes especially Markov chains. 
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Markov Chain
A Markov chain or Markov process is a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event
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Applications
NLP Algorithms (Finite State Transducers )
Engineering Physics (Brownian Motian)
Finance (Stock price movements )
Hidden Markov Model (HMM)
A hidden Markov model (HMM) is a statistical model that can be used to describe the evolution of observable events that depend on internal factors, which are not directly observable.
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Stochastic Process
A stochastic process is a collection of random variables used to represent the evolution of some random value, or system, over time.
Brownian Motion
Brownian motion as the source of randomness and uncertainty is used for most applications to real-world problems encountered in financial economics.
Bemoulli Process
Bemoulli Process is a finite or infinite sequence of binary random variables, so it is a discrete-time stochastic process that takes only two values, canonically 0 and 1.
Random Walk
Random walk theory suggests that changes in asset prices are random. This means that stock prices move unpredictably, so that past prices cannot be used to accurately predict future prices.
Rational Decision
Rational decision making is the opposite of intuitive decision making. It is a strict procedure utilising objective knowledge and logic.
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Importance
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Being aided by structured techniques, mathematics, and computers
Ongoing improvement when codified in a process, procedure, or program
Chaos Theory Chaos theory is an interdisciplinary area of scientific study and branch of mathematics. It focuses on underlying patterns and deterministic laws of dynamical systems that are highly sensitive to initial conditions.
Decision TheoryDecision theory (or the theory of choice) is a branch of applied probability theory and analytic philosophy concerned with the theory of making decisions based on assigning probabilities to various factors and assigning numerical consequences to the outcome
Probability Theory
Probability theory, a branch of mathematics concerned with the analysis of random phenomena. The outcome of a random event cannot be determined before it occurs, but it may be any one of several possible outcomes. The actual outcome is considered to be determined by chance.