hybrid bayesian network

This is an example input file for a “hybrid” Bayesian network, i.e., one with varying types of conditional probability distributions. It provides hybrid CPD data for the same graph skeleton as in the discrete case:

{
    "Vdata": {
        "Grade": {
            "parents": [
                "Difficulty",
                "Intelligence"
            ],
            "type": "lgandd",
            "children": [
                "Letter"
            ],
            "hybcprob": {
                "['high']": {
                    "variance": 10,
                    "mean_base": 20,
                    "mean_scal": [
                        1
                    ]
                },
                "['low']": {
                    "variance": 10,
                    "mean_base": 10,
                    "mean_scal": [
                        1
                    ]
                }
            }
        },
        "Intelligence": {
            "numoutcomes": 2,
            "cprob": [
                0.9,
                0.1
            ],
            "parents": null,
            "vals": [
                "low",
                "high"
            ],
            "type": "discrete",
            "children": [
                "SAT",
                "Grade"
            ]
        },
        "Difficulty": {
            "mean_base": 50,
            "mean_scal": [],
            "parents": null,
            "variance": 18,
            "type": "lg",
            "children": [
                "Grade"
            ]
        },
        "Letter": {
            "mean_base": -110,
            "mean_scal": [
                2
            ],
            "parents": [
                "Grade"
            ],
            "variance": 10,
            "type": "lg",
            "children": null
        },
        "SAT": {
            "parents": [
                "Intelligence"
            ],
            "crazyinput": 7,
            "type": "crazy"
        }
    }
}