Question

explain what AgenaRisk is and the entire procedures by your own words

2 paragraphs minimum, 5 max

Answer 1

AGENARISK provides Bayesian Network Software which can be used for Risk Analysis, Artificial intellegence and Decision Making applications.

It utilizes the most recent advancements from the field of Bayesian man-made consciousness and probabilistic thinking to demonstrate intricate, unsafe issues and improve how choices are made.
AgenaRisk models to cause expectations, to perform diagnostics and settle on choices by consolidating information k

customers use AgenaRisk to show an assortment of issues including danger and vulnerability including operational hazard, actuarial examination, insight investigation chance, frameworks wellbeing and dependability, wellbeing hazard, digital security chance and vital budgetary arranging.

ADVANTAGES

Offers numerous advantages over 'big data alone' approaches: It copes with incomplete data and represents real world causal interactions. Bayesian models can carry out prediction and abduction (diagnosis) simultaneously and combine both causal and statistical information

. • Combines the benefits of Bayesian Networks (referred to throughout this manual as Bayesian Networks, and also known as probabilistic graphical models), statistical simulation and decision analysis.

• Provides an extensive library of models covering a huge number of application areas including: Project risk, operational risk, stress testing, legal reasoning, medical diagnosis, financial decision making, value of information analysis and more.

• Is visual, easy to use, intuitive and powerfull

KEY FEATURES

Risk maps, a generalised form of Bayesian Networks for modelling causal and other relationships

• Risk graphs and statistics with zoomable and scalable graphs, histogram, and area plots, summary statistics and graphs overlays, as well as percentile and cumulative plots.

• Learning from data for Gaussian discrete nodes using the EM (Expectation-Maximization) algorithm and tailored using constructed models

. • Node probability tables, expression and partitioned expressions

. • Statistical distribution functions, including Normal, Beta, Binomial etc

. • Formulae expression parser including Noisy-OR, Ranked nodes, comparative and other mathematical functions.

• Sensitivity and multivariate analysis to assess impact of number of variables on one target variable. • Hybrid influence diagrams for decision making, producing Decision trees.

• Value of Information Analysis, to help determine how much to pay for information.

• Risk object modeling for modular model construction

. • Compound sum analysis for risk aggregation of event frequency and severity variables.

• Model library of comprehensive examples linked to tutorial material in book.

• Data import and export via CSV, HTML and JPEG files.