**we are unsure of the predicates**, when the possibilities of predicates become too large to list down, when it is known that an error occurs during an experiment. Bayesian network is a directed acyclic graph model which helps us represent probabilistic data.

Why Product performance is the key to brand success?

**why product functionality is the key to brand success**.

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It is undeniably a pillar of the field of machine learning, and many recommend it as a **prerequisite** subject to study prior to getting started. … The maximum likelihood framework that underlies the training of many machine learning algorithms comes from the field of probability.

Probabilistic reasoning is using **logic and probability to handle uncertain situations**. An example of probabilistic reasoning is using past situations and statistics to predict an outcome.

Reasoning under uncertainty is also known as probabilistic reasoning. Methods We discuss probabilistic reasoning in the context of a medical diagnosis or prognosis. … It **updates prior beliefs about diagnoses or prognoses in a coherent manner** and enables proper consideration of successive pieces of information.

. In the logic based approaches described, we have assumed that **everything is either believed false or believed true**. However, it is often useful to represent the fact that we believe such that something is probably true, or true with probability (say) 0.65.

Probabilistic reasoning is **a method of representation of knowledge where the concept of probability is applied to indicate the uncertainty in knowledge**. Probabilistic reasoning is used in AI: When we are unsure of the predicates. When the possibilities of predicates become too large to list down.

Probabilistic techniques **allow us to reason about all of the symptoms, including the uncertainty we have about whether they are actually there**. … and because for some application, it is hard to find the knowledge based techniques, the probabilistic approach is used and popular.

2. What is needed to make probabilistic systems feasible in the world? Explanation: **On a model-based knowledge** provides the crucial robustness needed to make probabilistic system feasible in the real world.

Thinking probabilistically means **having a willingness to always ask questions like “What else might happen**?”, “What could happen next?”, “What if we’re wrong?” and to look at the full range of possibilities that might come to pass rather than to assume that things will go as planned.

Commonly applied approaches to uncertainty reasoning include **probability theory, fuzzy logic**, Dempster-Shafer theory, and numerous other methodologies.

Introduction. Though there are various types of uncertainty in various aspects of a reasoning system, the “reasoning with uncertainty” (or “reasoning under uncertainty”) research in AI has been **focused on the uncertainty of truth value**, that is, to allow and process truth values other than “true” and “false”.

There are four methods of manage uncertainty in expert systems and artificial intelligence [23] [24]. They are: 1) default or non-monotonic logic, 2) probability, 3) fuzzy logic, 4) truth-value as evidential support, Bayesian theory, and 6) probability reasoning.

When talking about Artificial Intelligence, an **agent faces uncertainty in decision making when it tries to perceive the environment for information**. Because of this, the agent gets wrong or incomplete data which can affect the results drawn by the agent.

Generally and vaguely, I take them to embody what I shall call probabilistic inference. This form of inference is clearly **non-monotonic**. Relatively few people have taken this form of inference, based on high probability, to serve as a foundation for non-monotonic logic or for a logical or defeasible inference.

1. Which algorithm is used for solving temporal probabilistic reasoning? Explanation: **Hidden Markov model** is used for solving temporal probabilistic reasoning that was independent of transition and sensor model. 2.

In machine learning, a probabilistic classifier is **a classifier that is able to predict, given an observation of an input, a probability distribution over a set of classes**, rather than only outputting the most likely class that the observation should belong to.

Probabilistic approaches to real-world problems are omnipresent today. One of the key challenges in these approaches is **the representation of the joint probability of a set of random variables, whose size is exponential in the number of random variables**.

1 Answer. Some, but not all, of the machine learning models, are **probabilistic models**. There are machine learning models that are probabilistic by design, such as Naive Bayes.

What is the goal of artificial intelligence? Explanation: The scientific goal of artificial intelligence is **to explain various sorts of intelligence**. Explanation: An Algorithm is complete if It terminates with a solution when one exists.

4. What is used in determining the nature of the learning problem? Explanation: **The type of feedback** is used in determining the nature of the learning problem that the agent faces.

Answer:Artificial intelligence is **the simulation of human intelligence processes by machines**, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision.

With Bayes Theorem and estimated probabilities, **companies can better evaluate systematic changes in interest rates**, and steer their financial resources to take maximum advantage.

A Payoff Table is **a listing of all possible combinations of decision alternatives and states of nature**. The Expected Payoff or the Expected Monetary Value (EMV) is the expected value for each decision.

A deterministic methodology is **a method in which the chance of occurrence of the variable involved is ignored and the method or model used** is considered to follow a definite law of certainty, and not probability.

People arrive at conclusions only tentatively; based on partial or incomplete information reserve the right to retract those conclusions while they learn new facts. Such reasoning non-monotonic, precisely because **the set of accepted conclusions have become smaller when the set of premises expanded**.

Another form of reasoning involves reasoning about mutual causes of a common effect. This is called **inter causal reasoning**.

The main idea of utility theory is really simple: **an agent’s preferences over possible outcomes can be captured by a function that maps these outcomes to a real number**; the higher the number the more that agent likes that outcome. The function is called a utility function.

A logic is non-monotonic **if some conclusions can be invalidated by adding more knowledge**. … The logic of definite clauses with negation as failure is non-monotonic.

Classic deductive logic entails that **once a conclusion is sustained by a valid argument, the argument can never be invalidated, no matter how many new premises are added**. This derived property of deductive reasoning is known as monotonicity.

There are two different kinds of conflicts that can arise within a given non-monotonic framework: **(i) conflicts between defeasible conclusions and “hard facts**,” some of which possibly newly learned; and (ii) conflicts between one potential defeasible conclusion and another (many formalisms, for instance, provide some …