Multi-Agent Collaboration in Finance: Building Intelligent Teams with LLMs
Multi-agent systems are redefining how financial tasks like M&A analysis can be approached, using teams of collaborative LLMs with distinct responsibilities.
Recent posts
Predicting Hospital Readmissions for Elderly Patients Using Machine Learning
Machine learning models are revolutionizing post-hospitalization care by predicting hospital readmissions in elderly patients, helping healthcare providers optimize treatment and reduce complications.
Linear Optimization: Efficient Resource Allocation for Business Success
Learn how decision-makers in industries like logistics, finance, and manufacturing use linear optimization to allocate scarce resources effectively, maximizing profits and minimizing costs.
Chauvenet’s Criterion: A Statistical Approach to Detecting Outliers
Chauvenet’s Criterion is a statistical method used to determine whether a data point is an outlier. This article explains how the criterion works, its assumptions, and its application in real-world data analysis.
Exploring Kernel Density Estimation: A Powerful Tool for Data Analysis
Kernel Density Estimation (KDE) is a non-parametric technique offering flexibility in modeling complex data distributions, aiding in visualization, density estimation, and model selection.
Chi-Square Test: Exploring Categorical Data and Goodness-of-Fit
Dive into the Chi-Square Test, a statistical method for evaluating categorical data. Understand its applications in survey analysis, contingency tables, and genetics.
Peirce’s Criterion: A Robust Method for Detecting Outliers
Peirce’s Criterion is a robust statistical method devised by Benjamin Peirce for detecting and eliminating outliers from data. This article explains how Peirce’s Criterion works, its assumptions, and its application.
Dixon’s Q Test: A Guide for Detecting Outliers
Dixon’s Q test is a statistical method used to detect and reject outliers in small datasets, assuming normal distribution. This article explains its mechanics, assumptions, and application.
State Space Models (SSMs) in Time Series Analysis: Discretization, Kalman Filter, and Bayesian Approaches
State Space Models (SSMs) offer a versatile framework for time series analysis, especially in dynamic systems. This article explores discretization, the Kalman filter, and Bayesian approaches, including their use in econometrics.
Statistical AI: Probabilistic Foundations of Artificial Intelligence
Statistical AI leverages probabilistic reasoning and data-driven inference to build adaptive and intelligent systems.