Training Bayesian network with Infer.NET but doesn't know any prior and conditional probability

Question

Hi, I am new to Infer.NET. I know this question might be asked a few years ago but some posts' links were broken so I just ask here again. Let assume that if I have the whole dataset for training and I would like to construct a Bayesian network in Infer.NET without knowing any prior and conditional probabilities. I wonder how can Infer.NET infer those prior and conditional probabilities(because my inferred probabilities were wrong) and if I have multiple parents(e.g., 7 parents) for one child node, what kind of distribution should I define the prior probabilities of parents and the conditional probability of the child? Is it always a Dirichlet distribution represented in terms of Vectors or it should be something else?

Here is my naive code which tried to construct a Bayesian network like iMay ------> iSex:
using System;
using Microsoft.ML.Probabilistic.Models; //For modelling Bernoulli distribution
using Microsoft.ML.Probabilistic.Math;
using Microsoft.ML.Probabilistic.Distributions;
using Range = Microsoft.ML.Probabilistic.Models.Range;
using System.Data;
using System.IO;
using System.Linq;

namespace learningdotnet
{
public class BNmodel
{
// Primary random variables
public Variable<int> NumberOfExamples;
public VariableArray<int> iMay;
public VariableArray<int> iSex;
public Variable<Vector> ProbiMay;
public VariableArray<Vector> CPTiSex;
public Variable<Dirichlet> ProbiMayPrior;
public VariableArray<Dirichlet> CPTiSexPrior;

    public Dirichlet ProbiMayPosterior;
    public Dirichlet[] CPTiSexPosterior;

    public InferenceEngine Engine = new InferenceEngine();

    public BNmodel()
    {
        // Set up the ranges
        NumberOfExamples = Variable.New<int>().Named("NofE");
        Range N = new Range(NumberOfExamples).Named("N");

        // Variables have just 2 states (go to this attribute/not go to this attribute)
        Range M = new Range(2).Named("M");
        Range S = new Range(2).Named("S");

        // Define the priors and the parameters
        ProbiMayPrior = Variable.New<Dirichlet>().Named("ProbiMayPrior");
        ProbiMay = Variable<Vector>.Random(ProbiMayPrior).Named("ProbiMay");
        ProbiMay.SetValueRange(M);

        // iSex probability table conditioned on iMay
        CPTiSexPrior = Variable.Array<Dirichlet>(M).Named("CPTiSexPrior");
        CPTiSex = Variable.Array<Vector>(M).Named("CPTiSex");
        CPTiSex[M] = Variable<Vector>.Random(CPTiSexPrior[M]);
        CPTiSex.SetValueRange(S);

        iMay = Variable.Array<int>(N).Named("iMay");
        iMay[N] = Variable.Discrete(ProbiMay).ForEach(N);
        iSex = AddChildFromOneParent(iMay, CPTiSex).Named("iSex");
    }

    public static VariableArray<int> AddChildFromOneParent(
        VariableArray<int> parent,
        VariableArray<Vector> cpt)
    {
        var n = parent.Range;
        var child = Variable.Array<int>(n);
        ForEachBlock b1 = Variable.ForEach(n);
        SwitchBlock b2 = Variable.Switch(parent[n]);
        child[n] = Variable.Discrete(cpt[parent[n]]);
        b2.CloseBlock();
        b1.CloseBlock();
        return child;
    }

    public void LearnParameters(
        int[] imay,
        int[] isex,
        Dirichlet probiMayPrior,
        Dirichlet[] cptiSexPrior)
    {
        NumberOfExamples.ObservedValue = imay.Length;
        iMay.ObservedValue = imay;
        iSex.ObservedValue = isex;
        ProbiMayPrior.ObservedValue = probiMayPrior;
        CPTiSexPrior.ObservedValue = cptiSexPrior;

        // Inference
        ProbiMayPosterior = Engine.Infer<Dirichlet>(ProbiMay);
        CPTiSexPosterior = Engine.Infer<Dirichlet[]>(CPTiSex);
    }

    public void LearnParameters(
        int[] imay,
        int[] isex)
    {
        Dirichlet probiMayPrior = Dirichlet.Uniform(2);
        Dirichlet[] cptiSexPrior = Enumerable.Repeat(Dirichlet.Uniform(2), 2).ToArray();

        LearnParameters(imay, isex, probiMayPrior, cptiSexPrior);
    }

    public double ProbiSex(
        int? imay,
        Dirichlet probiMayPrior,
        Dirichlet[] cptiSexPrior)
    {
        NumberOfExamples.ObservedValue = 1;
        if (imay.HasValue)
        {
            iMay.ObservedValue = new int[] { imay.Value };
        }
        else
        {
            iMay.ClearObservedValue();
        }

        iSex.ClearObservedValue();


        ProbiMayPrior.ObservedValue = probiMayPrior;
        CPTiSexPrior.ObservedValue = cptiSexPrior;

        // Inference
        var iSexPosterior = Engine.Infer<Discrete[]>(iSex);
        // index 0 is true and index 1 is false
        return iSexPosterior[0].GetProbs()[0];
    }

    public double ProbiSex(
        int? imay,
        Vector probiMay,
        Vector[] cptiSex)
    {
        var probiMayPrior = Dirichlet.PointMass(probiMay);
        var cptiSexPrior = cptiSex.Select(v => Dirichlet.PointMass(v)).ToArray();
        return ProbiSex(imay, probiMayPrior, cptiSexPrior);
    }

}

public class BN
{
    public static void infer()
    {
        Rand.Restart(12347);
        BNmodel model = new BNmodel();

        double a = 1 - 0.9895496250434754;
        Vector probiMay = Vector.FromArray(a, 0.9895496250434754); //iMay, not iMay
        a = 1 - 0.9904474054066148;
        Vector[] cptiSex = new Vector[] { Vector.FromArray(0.9904474054066148,a) /* iMay */, Vector.FromArray(0.9999999961141224, (1 - 0.9999999961141224)) /* not iMay */ };

        double probNotiSexGiveniMay = model.ProbiSex(0, probiMay, cptiSex);
        double probiSexGivenNOTiMay = model.ProbiSex(1, probiMay, cptiSex);

        Console.WriteLine("P(Not iSex | iMay) = {0:0.0000}", probNotiSexGiveniMay);
        Console.WriteLine("P(iSex | not iMay) = {0:0.0000}", probiSexGivenNOTiMay);

        // -------------------------------------------------------------
        // Learn posterior distributions for the parameters
        // -------------------------------------------------------------
        Console.WriteLine("\n*********************************************");
        Console.WriteLine("Learning parameters from data ");
        Console.WriteLine("*********************************************");

        int[] imay_ = new int[] { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 };
        int[] isex_ = new int[] { 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0 };

        // Now see if we can recover the parameters from the data - assume uniform priors
        model.LearnParameters(imay_, isex_);

        // The posteriors are distributions over the probabilities and CPTs. Print out the means of these
        // distributions, and compare with the ground truth
        Console.WriteLine("Prob. iMay:                         Ground truth: {0:0.00}, Inferred: {1:0.00}", 1 - 0.9895496250434754, model.ProbiMayPosterior.GetMean()[0]);
        Console.WriteLine("Prob. iSex | iMay:                  Ground truth: {0:0.00}, Inferred: {1:0.00}", 0.9904, model.CPTiSexPosterior[0].GetMean()[0]);
        Console.WriteLine("Prob. iSex | Not iMay:              Ground truth: {0:0.00}, Inferred: {1:0.00}", 1.0000, model.CPTiSexPosterior[1].GetMean()[0]);
    }
}

}

At the last few lines of code, I tried to learn the prior and CPT probabilities from the dataset but the inferred probabilities are too different from the ground truth, which are:
Prob. iMay: Ground truth: 0.01, Inferred: 0.99
Prob. iSex | iMay: Ground truth: 0.99, Inferred: 0.52
Prob. iSex | Not iMay: Ground truth: 1.00, Inferred: 0.33

I wonder what is doing wrong here. Thank you so much for help!

Jingwen